JP2000003450A - Method for absorbing plural areas in color picture - Google Patents

Abstract

PROBLEM TO BE SOLVED: To execute a high speed processing and to reduce a processing cost by obtaining the peripheral length of a same label area and a common boundary length with an area being adjacent to the same label area, obtaining a connection degree between the adjacent areas based on the ratio of the peripheral length and the common boundary length and judging the relation of connection between the respective areas after-describing the connection degree in a memory. SOLUTION: Road candidate areas 113 and 114 are made reference, a low luminance area 119 and a high luminance area which are strongly connected to the road candidate areas 113 and 114 are absorbed in the road candidate areas and it is treated as one area and considered as a travelling course. For this, the common boundary length of the area adjacent to the respective areas and the peripheral length of the respective areas are obtained and the relation of the respective connected areas is described. Absorbing relation is expressed by the description of the relation of the areas. In this case, the connection degree between the adjacent areas is obtained based on the ratio of the peripheral length and the common boundary length and described in the memory so that the relation of connection in the respective areas is judged.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for merging a plurality of areas in a color image which is applied to a technique for discriminating a traveling path required for autonomous traveling of an automobile.

[0002]

2. Description of the Related Art Conventionally, most of the methods for extracting a travel road area are based on edge detection using a monochrome image, and almost all techniques for performing this type of travel road discrimination based on a color image have been provided. Absent.

When digital color image information is handled, RGB data (three primary color information-R;
In addition to red, G; green, B; blue), lightness I (the degree of color brightness), saturation S (the degree of color brilliancy), hue H (a value indicating the classification regarding the type of color), and the like. It is often used as a feature value. These characteristic amounts of lightness I, saturation S, and hue H are obtained by substituting the RGB data into a predetermined conversion formula. Conventionally, from this RGB data, IS
The conversion processing into H data has been performed every time ISH data is required in image processing. That is, when the ISH data becomes necessary, the CPU accesses the R image memory in which the R data is stored, the G image memory in which the G data is stored, and the B image memory in which the B data is stored. Collect data. And the CPU uses this R
The required ISH data has been calculated by performing a predetermined conversion process based on the GB data.

[0004] Conventionally, the discrimination of the traveling road area is performed based on the white lines and landmarks drawn on the traveling road by edge detection using a monochrome image, and based on the white lines and landmarks.

[0005] When an autonomous vehicle travels on a road in a general environment instead of a limited traveling course, various road surface conditions are conceivable. In particular, when various three-dimensional objects exist around the traveling course, a shadow is formed on the road surface.
If there is a shadow on the road surface, the traveling road area does not have a uniform color. Therefore, in order to extract the travel road area by image processing, it is necessary to take into account the shadow in the travel road area. Conventionally, in order to recognize the shadow, the road surface condition of the traveling course is limited, and the distribution of the sunlit portion and the shadow portion at the time of the limitation is measured in advance to extract the traveling road region. It is done in. In this method, the measured color distribution of each part is stored in a computer, and when an actual image is taken in, the stored color distribution is read out and compared with the color distribution of the input screen for running. This is to determine the road area.

Further, in order to determine the travel road area, it is necessary to label each area obtained by dividing the image and to classify each area. This labeling process is indispensable for calculating the area and the center of gravity of the target region in the binary image. Conventionally, labeling algorithms that have been proposed can be roughly classified into a raster scanning type, a run coat type, and a boundary tracking type. However, in the case of hardware (high-speed), a raster scanning type is generally used because of its real-time processing capability, circuit scale, ease of realization, and the like. In this scan, first, information indicating the connection between temporary labels having different values is held while allocating temporary labels in the first scan, and then the integrated information is analyzed, whereby the target area is one-to-one. Get the corresponding final label. Finally, labeling is performed by re-scanning the temporary label image obtained in the first scan and replacing the temporary label with the final label.

Further, when a given image is divided by a region dividing method, the image region is over-divided. For this reason, based on the color of each over-divided region and the degree of connection between each region and the surrounding region, the merging process of each over-divided region is performed. That is, after labeling the divided areas individually, the connection relationship between the labels is described. Then, based on this description, a label change operation is performed to rewrite the label of the merged area with the label of the merged area. The label change is performed by changing the label value, that is, the pixel value of a pixel in a region to be merged by scanning the image.

Conventionally, when a road edge or a running course is obtained from an image division area, a boundary between the obtained running course area and the background area is tracked to obtain a point sequence group corresponding to the boundary line. . That is, the pixels located at the boundary of the traveling area are tracked one by one along this boundary, the trace of this tracking is used as a group of points, and the road edge is determined based on the group of points.

[0009]

[0005] Performing such image processing using a color image is more costly than performing processing using a monochrome image. Is less.

[0010] Also, the IHS
The process of converting to data is performed every time IHS data is required in image processing, so that it takes time for the process. In particular, the conversion process from the RGB data to the hue data H takes a very long time. For example, even in the case of a workstation (Sun3), this conversion process takes about one minute. For this reason, in image processing such as autonomous driving of a car that requires real-time processing, it takes a long time for arithmetic processing, and sufficient running control cannot be performed.

In the above-described conventional method using edge detection in a monochrome image, a running course is recognized by tracing a white line or a landmark drawn on the running path. I need. In addition, as a result of processing an image captured by a camera, the detected edge may be interrupted due to a state of a light source that irradiates the traveling path or an unclear white line. It is difficult to detect the area.

Further, when there is dust or shadows on the traveling road, a white line or a landmark is erroneously detected, and accurate traveling road information may not be obtained. In addition, when the boundary between the traveling road area and the background area is divided by grass or soil, the change in the edge corresponding to the boundary is small, and it is difficult to recognize the boundary. Also, if the brightness of the running path changes every moment due to the weather or the position of the sun,
If the threshold value for the area division between the traveling road area and the background area is fixed, it becomes impossible to obtain a correct traveling road area.

Further, as described above, on a general road, the distribution of brightness on the road surface changes every moment due to the weather, the position of the sun, the movement of clouds, and the like during traveling. For this reason, in the conventional method in which the color distribution is measured in advance and compared with the input image, this comparison / determination process cannot follow the above change in brightness, and the shadow portion is not necessarily extracted. Not always. Further, when a part of the road is discolored due to pavement work or the like, the road area of the discolored portion may not be extracted by image processing depending on the degree of the discoloration.

Further, the images to be subjected to the labeling process up to now are all binary images, and labeling cannot be performed on multi-valued images. Further, in the raster scanning type, it is not possible to attach a label corresponding to the target area in one-to-one correspondence in one scan. Also, hardware can be relatively easily implemented for primary labeling and secondary labeling, but it is difficult to implement hardware for analysis of integrated information, that is, integrated processing. In the raster scanning type algorithm, a large amount of integrated information is generated at the time of provisional labeling, so that the load of the integration processing is large and the processing performance as a whole in the labeling processing is not improved.

The conventional label value changing operation for area merging is performed by scanning all pixels of an image or at least pixels in a rectangle circumscribing the merging area. Therefore, it takes time to scan the label replacement, and it becomes difficult to perform high-speed processing required for autonomous traveling of the automobile. There is also a disadvantage that the processing cost is not reduced.

Further, the above-described conventional method of sequentially tracking the pixels at the boundary of the area one by one to determine the road edge requires a long time for processing. In addition, when there are a plurality of areas, there is a problem as to what is used as a basis for determining the road edge.
Further, a case where the traveling course region and the divided region are very slightly connected and the divided region is not the traveling course may occur depending on the result of the image processing. In such a case, in the conventional method of sequentially tracking the boundaries of the regions, the boundaries of the regions that are not actually on the traveling course are also obtained as point sequences. As a result, accurate road edge information cannot be obtained. Further, even if the obtained point sequence groups are separated without being connected, it is necessary to verify the validity of the plurality of point sequence groups, and the processing becomes complicated. Furthermore, if the obtained image has holes or cuts due to noise, the obtained point sequence group will not be smooth.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a method for merging a plurality of areas in a color image which can perform high-speed processing and has a reduced processing cost. It is in.

[0018]

SUMMARY OF THE INVENTION In order to achieve the above-mentioned object, the present invention provides a method of scanning a mask for pixels in each of image-divided and labeled regions, thereby obtaining a perimeter of the same label region and the same label. The common boundary length between the region adjacent to the region is obtained, the connection between the adjacent regions is obtained based on the ratio of the perimeter to the common boundary length, and the connection is described in the memory to connect the regions. The first feature is that the relationship is determined.

Also, a pixel number indicating the position is assigned to each pixel located in the vicinity of the pixel of interest, and a pixel reference table in which the position numbers are arranged in the column direction and the row direction in a predetermined order is created in advance.
The peripheral length of the region and the common boundary length between adjacent regions are obtained by searching for pixels near the pixel at the boundary of the region based on the pixel reference table, and based on the ratio between the peripheral length and the common boundary length. A second feature is that the degree of connection between adjacent areas is obtained, and the degree of connection is described in a memory to determine the connection relationship between the areas.

According to the above feature, the peripheral length of the region and the common boundary length between the adjacent regions are obtained, and the connection between the adjacent regions is obtained based on the ratio of the peripheral length to the common boundary length. Is described in the memory to determine the connection relationship between the respective areas. Further, the merging relationship between the divided areas is determined without performing the label replacement scan. For this reason,
High-speed processing becomes possible.

[0021]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A case where a color image processing apparatus and a processing method according to one embodiment of the present invention are applied to traveling control of an autonomous traveling vehicle will be described below. With the apparatus and method of the present embodiment, the traveling course of the traveling vehicle is automatically recognized, and the traveling vehicle determines the steering angle of the steering, the fuel injection amount to the engine, etc. based on the recognized traveling course, and performs autonomous traveling. I do.

FIG. 1 is a block diagram showing a schematic configuration of the entire color image processing apparatus according to this embodiment. A color image processing apparatus includes a color camera 101 that captures road information.
An ISH conversion unit 102 for performing ISH conversion of captured RGB information; a color processing unit 103 for extracting a road candidate region and the like from ISH-converted image information; and a labeling hardware unit for performing labeling processing on a color-processed image. 104, and a CPU processing unit 105 that executes a merging process or the like on the labeled image area. I
The SH conversion unit 102 includes a color camera input board, an ISH
It is composed of a conversion board, a filter and the like.

FIG. 2 is a schematic flowchart showing an image processing method in the color image processing apparatus. The road image 106 is captured as RGB information by the color camera 101, and is converted into brightness (I), saturation (S), and hue (H) images by the ISH conversion unit 102 (step 201). On the basis of these images, the color processing unit 103 extracts a road candidate area serving as a basis of the traveling course (step 202). Here, it is determined whether or not the low-luminance threshold value presence flag in the status register of the CPU is turned on (step 203). This flag is a flag indicating whether or not the captured original image has a low luminance region such as a shadow or a discolored portion. If the flag is on, the low luminance region is extracted by the color processing unit 103 (step 204). The extracted road candidate area and low-luminance area are labeled in the labeling hardware unit 104, and the connection relation between the areas is determined. Based on this determination result, when the respective areas are in a relationship to be merged, the CPU processing unit 105 executes a merge (merge) process (step 205).

Next, it is determined whether or not the high luminance threshold value presence flag in the status register of the CPU is turned on (step 206). If the low luminance threshold existence flag is not turned on in step 203,
The process of step 206 is immediately executed. This high-luminance threshold value presence flag is a flag indicating whether or not the captured original image has a high-luminance area such as a sunlit area or a discolored portion.
, A high-luminance area is extracted (step 207).
The extracted road candidate area and high brightness area are labeled in the labeling hardware unit 104, and the connection relation between the areas is determined. On the basis of this determination result, when the respective regions are in a relationship to be merged, a merge (merge) process is executed in the CPU processing unit 105 (step 20).
8).

Based on the road candidate areas merged in this way, the left and right border edges of the area, that is, the border lines of the road edges are obtained as a point sequence. Based on this point sequence information, a traveling course based on the original image captured this time is recognized (step 209). Thereafter, the process returns to step 201,
The same processing as described above is repeatedly executed for image information obtained subsequently with the movement of the autonomous vehicle, and the travel control of the autonomous vehicle is executed.

Next, the above processing will be described in more detail with reference to the block diagram of the color processing apparatus shown in FIG. The color camera 101 is fixedly installed on the body of the autonomous traveling vehicle. The color camera 101 captures a road image 106 located in front of the traveling vehicle as RGB information. The RGB information is given to the conversion processing unit 107 of the ISH conversion unit 102. This conversion processing unit 10
7, an “ISH conversion process of a color image using a ROM table” to be described later is executed, and the RGB road image information includes a lightness (I) image 108 and a
9 and the hue (H) image 110.

The brightness image 108 is extracted from the road candidate area extracting means 1
11, a road candidate area image 113 based on the brightness image is extracted by a “running course extraction method using repeated threshold processing using a template image” described in detail later. The saturation image 109 is provided to the road candidate area extracting means 112, and a road candidate area 114 based on the saturation image is extracted by the same method as described above. The threshold value for the area division in this method is determined by “threshold setting means based on the shape of the feature amount histogram in the repeated threshold processing” described later. Each of the obtained road candidate area images 113 and 114 is a logical product
15 and the common part of each road candidate area obtained from the brightness and the saturation is extracted, and becomes a new road candidate area image 116 based on the color information.

When a low luminance area exists in the brightness image 108, a low luminance threshold existence flag in the status register 117 of the color processing unit is turned on.
When this flag is on, the low-luminance area extracting means 118 takes in the brightness image 108. Then, a low-luminance area is extracted by “extraction means of a shadow or a high-luminance portion from a traveling course paying attention to a difference in brightness” which will be described in detail later.
The extracted low luminance area becomes the low luminance image 119. The low-luminance area image 119 is output to the road candidate area image 1 extracted from the saturation image 109 by the AND operation means 120.
The logical product of the two is obtained by taking the logical product of the low-brightness area and the saturation area similar to the road candidate area. The extracted image information of the low luminance region is added to the road candidate region image 116.

If a high-luminance area exists in the brightness image 108, a high-luminance threshold value flag in the status register 117 of the color processing unit is turned on.
When this flag is on, the high-luminance area extraction means 121 takes in the brightness image 108. Then, a high-brightness area is extracted by “extraction means of a shadow or a high-brightness portion from a traveling course focusing on a difference in brightness” which will be described in detail later.
The extracted high brightness area becomes the high brightness area image 122.
The high-brightness area image 122 is ANDed with the road candidate area image 114 extracted from the saturation image 109 by the logical product calculation means 123, and has a similar saturation to the road candidate area in the high-brightness area. The region is extracted. The extracted image information of the high luminance area is added to the road candidate area image 116.

By the above means, the road candidate area image 11
6 includes a road candidate area image 113 with a pixel value of 1, a low-luminance area image 119 with a pixel value of 2, and a high-luminance area image 122 with a pixel value of 3.

The road candidate area image 116 is provided to the labeling processing section 124. The labeling processing unit 124 labels each area of the given image, and
Create 6. At the same time, the labeling processing unit 124 calculates the area and the center of gravity of the same label area.
Each of these calculated values becomes a calculated value 125 corresponding to the labeling processing unit 124. This labeling processing is executed by a “labeling processing device” described in detail later.

The CPU fetches the label image 126 into the small area removing means 127. Here, it is assumed that a small region caused by noise or the like or a region whose center of gravity is located above the horizon does not correspond to a road region, and these regions are removed from each label image. Small area removing means 12
7, the label image from which the small area has been removed from the road candidate area becomes a new label image 128. Also, out of the calculated values 125 for each label attached to each area, the feature amount of each area not removed by the small area removing means 127 is described in List 1.

Based on the feature amount 129, the label image 128, and the road image 116 stored in the list 1, each label region of the road candidate region image, each label region of the low luminance region image, and each label of the road candidate region image If the area and the label areas of the high-brightness area image are in a relationship to be merged, the area merging unit 130 performs merging processing,
The new label image 131 is stored in the memory 1. The above-described merging process is described in detail in "Means for Merging Multiple Areas".
Is executed by

Based on the finally obtained label image 131, road position coordinates 132 at the left and right ends of the road area are calculated. Based on the road end position coordinates 132, point sequence data 133 corresponding to the road end is obtained. The process of obtaining the road edge is executed by "means for obtaining a travelable range" described later in detail, and "internally expressing a travel course of various shapes". The point sequence data 133 is transmitted to a data management unit that performs overall management of image processing data for traveling control of the autonomous traveling vehicle, and the color image processing ends.

Next, R obtained by imaging with a color camera
The "ISH conversion process of a color image using a ROM table" for converting the GB data into data of lightness I, saturation S, and hue H will be described below with reference to FIG.

First, the original image 301 is converted to R
Input as GB data. Since each of the RGB data is mixed in the original image 301, each component of the RGB data is separated. Then, the separated RGB data are stored in the R image memory 302, the G image memory 303, and the B image memory 3 respectively.
04 are stored separately in each of the three image memories. Each of these R, G, B image memories 302 to 304 is composed of a plurality of pixel values having 8-bit gradation, and is subjected to ISH conversion as follows.

First, when reading each of the 8-bit RGB data, only the upper 6 bits are read, and the lower 2 bits are discarded. In other words, by taking the upper 6 bits,
The pixel value of eight gradations is approximated to the pixel value of six gradations. The numerical value of these upper 6 bits is 00-3F (hex) in hexadecimal.
(Changes between 00 and FC (hex) considering the truncated lower 2 bits). In addition, numerical values obtained by dividing the value of the upper 6 bits by 40 (hex) are defined as R, G, and B values, respectively. Each of these R, G, and B values changes in the range from a real number 0 to about 1.

Assuming that the values obtained by dividing the values of R, G, and B by (R + G + B) are r, g, and b, respectively, the conversion from the R, G, and B values to the I, S, and H values is performed according to the following equation. Is Here, min (r, g, b) indicates the value of the feature value having the minimum value among the feature values of r, g, and b. I = (R + G + B) / 3 (1) S = 1− (1/3) · min (r, g, b) H = 1/2 + (1 / π) · arctan ｛(3) 1/2・ (G-
b) / (2r−g−b)｝ This conversion is performed for each of R, G, and B pixels in raster scan order, and the converted I, S, and H values all take values from 0 to 1. Become a real number. The conversion table data from RGB to brightness I is stored in the ROM 305, and the RGB is stored in the ROM 306.
, And conversion table data from RGB to hue H are stored in the ROM 307. Each of these ROMs 305 to 307 functions as a look-up table. In addition, each of the ROMs 305 to 307
The address at which data is stored in each of the R, G, and
It is determined by the upper 6 bits of the 8-bit numerical value of the B value. The storage capacity of each of the ROMs 305 to 307 is 18 bits (= 256 Kbytes).

Each feature amount stored in the ROMs 305 and 306 is stored in the brightness data 30 in response to an instruction from the CPU.
8. Immediately read out as chroma data 309 and supplied in real time to required image processing. Also, R
The feature amount of the hue data 310 read from the OM 307 is further averaged by removing noises by a 3 × 3 average value filter 311. Therefore, the hue data 312 used for each image processing is smoothed and noise-free data. Each of the read feature amount data (brightness 308, saturation 309, hue 310 and 312) is approximate 8-bit data in which the lower 2 bits are 0 and the upper 6 bits are valid.

Next, an example of ISH conversion processing using the present algorithm will be described with reference to FIGS. 4 to 7 while comparing with an ISH conversion processing example not using the present algorithm. (A) of each figure is an outline of an original image captured by a color camera. That is, FIG. 4 (a) shows a scene in which the traveling path curves far away, and a guardrail is installed on one side of the traveling path, and trees are overgrown in the far side of the guardrail. FIG. 5 (a) shows a traveling road where the traveling road edge is partitioned by weeds and the like, in a distant place with dwellings, trees and the like. FIG. 6 (a) shows a running road on a highway at night, where the road surface is a scene lit by moonlight and a small amount of light by a lamp.
FIG. 7 (a) shows a daytime running path in good weather, where the tree is shaded by trees in a block wall on the running path.

Further, (b-) in each of FIGS.
1) and (b-2) are histograms using lightness I as a feature amount, (c-1) and (c-2) in each diagram are histograms using saturation S as a feature amount, and (d-1) in each diagram. ) And (d)
-2) is a histogram using the hue H ′ as a feature before being applied to the 3 × 3 average filter 311, and (e−1) and (e−2) in each figure are after being applied to the 3 × 3 average filter 311. 3 is a histogram using the hue H of the characteristic amount as a feature amount.

The vertical axis of each histogram indicates the number of pixels of each feature amount, with 1/4 of the entire screen being the maximum. The horizontal axis of each histogram indicates the degree of each feature amount of lightness I, chroma S, hue H ', and H. The degree of each feature amount is displayed so as to increase as the distance from the origin increases.
The degree of each feature amount assigned to each numerical value of (hex) is divided into 64 and displayed. The number of pixels of the original image is slightly smaller than 512 × 512. This is because there is a portion where all the data of R, G, and B are 0 in the peripheral portion of the image, and each histogram is represented by a data value including this portion.

Further, (b-1), (c-1),
(D-1) and (e-1) are histograms obtained based on the conventional method. The CPU directly accesses each image memory and converts the R, G, B data into I, S, H according to the conversion formula.
It is obtained by converting to data. On the other hand, (b-2), (c-2), (d-2),
(E-2) is a histogram obtained based on the algorithm according to the method of the present embodiment. A ROM stores a conversion table from 6-bit gradation RGB data to each of the I, S, and H feature values. This is obtained by reading this.

Each of (b-1) and (b-) shown in FIGS.
As shown in 2) and (c-1) and (c-2), it can be seen that there is no large difference between the histogram distribution according to the present method and the histogram distribution according to the conventional method for the lightness I and the saturation S. . This indicates that the function according to the present method of converting RGB data into ISH data has no inferior aspect to the conversion function according to the conventional method. On the other hand, 3d shown in (d-1) and (d-2) of each figure
The overall distribution of the histogram distribution of the hue H ′, which is the feature amount before being applied to the × 3 average value filter 311, is different between the conventional method and the present method. This is because, in the image used in this example, the values of (g−b) and (2r−g−b) in the equation for converting the RGB data to the hue H take only very limited values near 0. This is because information is extremely discretized by the compression of the data into 6 bits according to the present method.

However, the histogram distribution obtained by applying the hue H ′ to the 3 × 3 average filter 311 is based on the conventional method as shown in (e-1) and (e-2) of each figure. It corresponds well to the distribution. The calculated value of the hue data is an unstable value that easily changes due to the small noise of the RGB data, and the hue image has a very large noise. For this reason, it is appropriate to perform some kind of smoothing on the calculated value result of the hue conversion as in the present method, and by performing this smoothing, the feature amount is compressed to 6 bits and processed. It is understood that no problem occurs. Since the calculated value of the hue is an unstable value that easily changes due to noise, the same distribution as the histogram distribution by the conventional method was obtained by passing the data of the hue H ′ through the average value filter.

As described above, the I, S, and H feature values are converted in advance using the ROMs 305 to 307 as a look-up table. It is no longer necessary for the CPU to access and perform calculations. As a result, the arithmetic processing speed at the time of data conversion according to this embodiment can be executed at a high video rate, and the processing speed is improved. Further, even if the conversion formula from the R, G, B data to the I, S, H data is changed, it is possible to cope with the same hardware. That is, the effect of the change of the conversion formula is only the change of the storage contents of the ROMs 305 to 307. For this reason, the hardware is not changed by changing the conversion formula. Further, since each pixel value of R, G, and B is compressed to 6 bits, the amount of hardware can be reduced. Further, when the hue H is read from the look-up table, by passing the data through the 3 × 3 average value filter 311, the adverse effect of compressing the data to 6 bits,
For example, it is possible to prevent the histogram from being discretized.

Next, the "threshold setting means based on the shape of the feature amount histogram in the repetitive threshold processing" will be described. This means is performed as a pre-process of the color image processing for extracting the travel road area. FIG. 8 is a flowchart showing an outline of this process, and shows the process when the contrast of the original image data obtained from the color camera installed in the traveling vehicle is low.

First, the RGB of the original image obtained from the camera
On the basis of the digital image data, a histogram representing the frequency with respect to the color feature (brightness or saturation) is created. The horizontal axis of this histogram is set to the color feature (lightness or saturation), and the vertical axis is set to the frequency of the feature. Since the contrast of the original image is low, the histogram is distributed toward the origin of the histogram. On the other hand, an image having a low contrast tends to have a single-peak mode, and a clear valley does not occur. For this reason, in general, a histogram is created after performing a predetermined operation on each pixel value and performing image enhancement. Image enhancement is performed by simply stretching (step 801).

Then, the left end processing (step 802) and the right end processing (step 80) of the emphasized histogram
Execute 3). Next, from the frequency distribution state of the histogram, a temporary peak (peak) having a high frequency for the feature amount,
A temporary valley having a low frequency with respect to the feature amount is set on the peak / valley table (step 804). The valley is evaluated on this table based on the obtained temporary valley frequency (step 805). Further, the valley is evaluated again on the table based on the peak adjacent to the valley (step 806). Finally, the evaluated peak / valley table is smoothed, and a valley effective for the division between the target region of the region division and the background region is extracted from the relative relationship between the peak and the valley (step 807).

On the other hand, in parallel with the above processing, step 80
Otsu's discriminant analysis method is applied to the histogram created in step 1 to obtain a region segmentation threshold value by this discriminant analysis method. Then, the obtained threshold value is compared with the position of the valley extracted in step 807, and the frequency of the valley located near the threshold value is set as a true threshold value. Correct the value.

Next, details of each process will be described below. The setting of the temporary peak and the temporary valley in step 804 is performed as follows. That is, provisional peaks and valleys are determined based on the frequency distribution state for the feature amount. Specifically, the states of the peaks and valleys are shown in FIG.
(F) shown in FIG. Here, let the point of interest on the histogram be hi (the subscript i means any one point when N points are equally allocated along the horizontal axis of the histogram, and have a value of 0 to N-1). ), An adjacent point having a feature amount smaller than the point of interest hi is defined as hi-1, and an adjacent point having a feature amount larger than the point of interest hi is set as hi + 1. The difference between the frequencies of the point of interest hi and the adjacent point hi-1 is determined by the pitch pi1 (pi1
= Hi-hi-1), and the difference in frequency between the adjacent point hi + 1 and the point of interest hi is defined as a pitch pi2 (pi2 = hi + 1-hi).

In the range of i = 1 to N-2 (excluding both end points of the histogram), the peak / valley table value pkti
Is set as follows. That is, the sign of pi1 and pi2
(1) If pi1 ≧ 0 and pi2 ≦ 0, table value pkti = 1 (2) If pi1 ≦ 0 and pi2 ≧ 0, table value pkti = −1 FIG. (A) is a state where pi1> 0 and pi2 = 0, and therefore, the table value pkti = 1. FIG. 7B shows a state where pi1> 0 and pi2 <0, and therefore, the table value pkti = 1. FIG. 3C shows that p
i1 = 0 and pi2 <0, so the table value pkti = 1. FIG. 9D shows a state where pi1 <0 and pi2 = 0, and therefore, the table value pkti
= -1. FIG. 9E shows that pi1 <0 and pi2>
0, so the table value pkti = -1. FIG. 11F shows a state where pi1 = 0 and pi2> 0, and therefore, the table value pkti = -1.

In this way, from the relation of the relative frequencies of the adjacent points, the states of FIGS. 7A, 7B and 7C are calculated as pkti = 1, and the point of interest hi is calculated as It is determined to be a temporary peak. In the states (d), (e), and (f) of FIG. 11, it is calculated that pkti = -1, and the point of interest hi is determined to be a temporary valley.

The valley evaluation process based on the frequency in step 805 is performed as follows. That is, i = 1 to
When the point of interest hi is a temporary peak at N-2 (p
kti = 1), (1) If the ratio between the adjacent point hj and the point of interest hi is smaller than 0.1 (hj / hi <0.1), and the adjacent point hj is on the left side of the point of interest hi, the adjacent point hj Table value pk corresponding to
tj is set to -1 (pktj = -1). (2) If the ratio between the adjacent point hm and the point of interest hi is smaller than 0.1 (hm / hi <0.1), and the adjacent point hm is on the right side of the point of interest hi, the table value corresponding to the adjacent point hm pk
tm is set to -1 (pktm = -1).

The valley evaluation processing based on the adjacent peak in step 806 is performed as follows. i = 0
In N-1, (1) When the point of interest hi is a temporary valley (pkti = -1), a temporary peak (neighboring point hk, table value pk) on the left side of the valley
tk = 1) is called topL. A temporary peak on the right side of the valley (neighboring point hj, table value pk
tj = 1) is called topR.

(2) topL and top are set at the point of interest hi.
When R is present together, the ratio l (l = hi / top) between the point of interest hi and topL
L) is the ratio r (r = hi / to) between the point of interest hi and topR.
pR) (l <r), the table value pktj
Is added to. If the ratio l (hi / topL) is larger than the ratio r (hi / topR) (l ≧ r), 1 is added to the table value pktk. (3) If both topL and topR exist at the point of interest hi and the ratio l <0.5 or the ratio r <0.5,
1 is subtracted from the table value pkti corresponding to the point of interest hi. (4) In cases other than the above (3), the table value pkt
Rewrite i to -4.

(5) A provisional peak topR only on the right side of the valley
Exists, if the ratio r <0.5, the table value pkt
Subtract 1 from i. (6) When the temporary peak topL exists only on the left side of the valley, if the ratio l <0.5, 1 is subtracted from the table value pktj corresponding to the adjacent point hj.

The smoothing process of the peak / trough table in step 807, that is, the peak / trough table p
At kt, when the distance between the obtained valleys is sufficiently short, the smoothing process is performed as follows.

At i = 1 to N-1, the table value pkti corresponding to the point of interest hi is -2, and the table value pktj of the valley located on the right side of the valley is also -2. (1) If the frequency of the point of interest hi is higher than the frequency of the adjacent point hj (hi> hj), the table value pkti is set to 0. (Pkti = 0). (2) If the frequency of the point of interest hi is lower than the frequency of the adjacent point hj (hi.ltoreq.hj), the table value pktj of the valley located on the right side is set to 0 (pktj = 0).

Next, a specific example using the above method will be described below. For example, assume that the feature amount histogram shown in FIG. 10A is obtained. The horizontal axis in the figure is the lightness or chroma color feature amount, and the vertical axis is the frequency of the feature amount in the image. When the processing shown in FIG. 8 is performed on this histogram, the table values of the peak / valley table pkt shown in FIG. The transition is as follows along the number 8. As a result of this transition, the numerical value shown in the lower row is obtained as the final table value. The positions at which the table values in FIG. 3B are described correspond to the feature amounts A to L of the histogram in FIG. That is, each of the listed table values corresponds to the feature value above the described position, as indicated by the dotted line.

First, the left end processing and the right end processing of the feature amount histogram are executed (steps 802 and 803), and then a temporary peak and a temporary valley are set (step 80).
4). This setting is performed in accordance with the processing of step 804 described above, and the table values obtained as a result are as shown in the table of No. 1. Next, valley evaluation based on frequency (step 805) and valley evaluation based on adjacent peaks (step 806) are performed according to the above-described processing.

When the feature amount is a valley of A, to the right of the valley is to
Only pR exists, and the ratio r between the frequency in the feature value A and the frequency of the temporary peak on the right side of the valley is 0.5 or less (hi / topR <0.5). For this reason, 1 is subtracted from the table value corresponding to the feature value A, and as a result, the table value becomes -2, and the table changes to the table indicated by No. 2.

When the feature amount is a valley of D, both topL and topR exist on both sides of the valley. Moreover, the ratio 1 between the frequency of the valley and the frequency of the temporary peak on the left side is 0.5 or less (hi / topL <0.5). Therefore, 1 is subtracted from the table value pkti corresponding to the feature value D, and as a result, the table value becomes -2. Further, the ratio r and the ratio r between the frequency of the temporary peak value and the frequency of the valley on both sides of the valley having the feature amount D are larger (l <r). Therefore, by adding 1 to the table value pktj of the temporary peak (feature value E) located on the right side of the valley, the table value becomes 2. As a result, the peak / valley table is number 3
It becomes the table shown in.

Further, both sides of the valley of the feature value F are to
There are pL and topR, and one of the ratios l or r is less than 0.5 (l, r <0.5). Therefore, 1 is subtracted from the table value pkti corresponding to the feature value F, and the table value is set to -2. Further, the ratio l is larger than the ratio r (l ≧
r). Therefore, 1 is added to the table value pktk of the temporary peak (feature amount E) located on the left side of the valley, and the table value is set to 3. As a result, the table changes to the table indicated by No. 4.

Further, both sides of the valley having the characteristic amount H are to
pL and topR are present and one of the ratios l or r is greater than 0.5 (l, r ≧ 0.5). Therefore, the table value pkti corresponding to the feature amount H is rewritten to -4.
Further, the ratio 1 is smaller than the ratio r (l <r). Therefore, 1 is added to the table value pktj corresponding to the temporary peak (feature amount I) on the right side of the valley. As a result, the table value corresponding to the feature value I becomes 2, and the table changes to the table indicated by No. 5.

Further, both sides of the valley of the feature value J are top.
L and topR are present and the ratio r is less than 0.5 (r <0.5). Therefore, 1 is subtracted from the table value pkti corresponding to this valley. The ratio 1 is larger than the ratio r (l ≧ r). Therefore, 1 is added to the table value pktk corresponding to the temporary peak (feature amount I) located on the left side of the valley, and the table value is set to 3. As a result, the table changes to the table indicated by No. 6.

Further, the valley of the characteristic amount L is topL on the left side of the valley.
And the ratio l is less than 0.5 (l <0.
5). Therefore, 1 is subtracted from the table value pkti corresponding to this valley. As a result, the table value becomes -2, and the table transits to the table indicated by number 7.

Next, the data of the peak / valley table No. 7 thus obtained is smoothed as described above (step 807). That is, the distance between the valley hi of the feature D and the valley hj of the feature F located on the right side thereof is smaller than the predetermined threshold value, and the table value of each valley is -2. Further, the frequency of the valley of the feature F is
Greater than the valley frequency (hi ≤ hj). Therefore, the table value pktj corresponding to the valley of the feature value F is set to 0. As a result, the table finally becomes the table shown at the bottom of FIG. 10 (b).

In the final table, the feature values A,
Each point on the histogram corresponding to D, H, J, and L is determined as a valley, but valleys A and L at both ends of the histogram are not targeted. ) Is not included. That is, the valleys effective for dividing the data image into the target area and the background area are valleys corresponding to the feature values D and J in which the table value-2 is surrounded by double circles. Of these valleys, the frequency of valleys close to the region segmentation threshold obtained by applying the Otsu discriminant analysis method to the histogram of FIG. 10 (a) becomes the true region segmentation threshold.
By correcting the ambiguous result of Otsu's discriminant analysis method in this way, it is possible to perform a highly accurate traveling road discrimination with a small error.

Next, a description will be given of a "running course extraction technique by repeated threshold processing using a template image". In the following description, it is assumed that the autonomous traveling vehicle travels on a course sectioned by grass, soil, and the like, and a case in which a traveling road region is extracted will be described.

FIG. 11 is a flow chart showing the outline of the algorithm of the road recognition.

First, the scene of the traveling road is imaged by the color CCD camera mounted on the traveling vehicle (step 110).
1). Then, the captured RGB color image signal is taken into the color camera input device, and the taken RGB original image data is converted into ISH data as described above (step 1102). Based on the feature image converted into the ISH data, a histogram representing the characteristics of each feature and the number of pixels is created by the color image processing apparatus as described above (step 1103).

Next, based on the created histogram, the threshold value processing is repeatedly applied to extract the traveling road as a binary image. Since the image thus extracted has noise and finely divided regions, the following processing is executed.
That is, a label is attached to each area by a labeling device described later in detail (step 1104). Then, the area and the center of gravity of each labeled area are measured, and the area whose center of gravity is higher than the horizon position calculated from the mounting position of the camera and the area whose area is small are removed (step 11).
05).

Next, based on the traveling road image first extracted by the color image processing apparatus, a region darker and brighter than the traveling road region are obtained, these regions are merged, and each region is combined. Is described (step 1106). A travelable range, that is, a road edge is detected based on the description of the area merging and the area relationship, and an image capture time is added to the travelable range information. Then, the travelable range information is transmitted to the data management unit included in the image processing apparatus (step 1107). Thereafter, the process returns to step 1102 to repeat the above processing.

FIG. 12 is a block diagram showing details of the processing for extracting the travel road area. First, brightness data I and saturation data S are obtained from the RGB data of the captured original image, and each data is stored in the image memory as a feature amount image (block 1201). Next, based on the brightness data I stored in the image memory, a histogram having brightness as a feature amount is created (block 1202). The number of divisions of the feature quantity on the horizontal axis of this histogram is 40 points, and this number of points is large enough to globally extract the traveling road. Next, the generated histogram is normalized (block 1203). Then, a well-known Otsu's discriminant analysis method is applied to the normalized histogram to calculate a threshold value for distinguishing the travel road region from the background region (block 1204).

On the other hand, a peak having a large number of pixels of the feature amount and a valley having a small number of pixels of the feature amount are obtained as described above based on the shape of the created histogram. (Block 120)
5). The value of each feature amount at each peak and valley is set as a candidate value for threshold processing.

Since the threshold value obtained by the Otsu discriminant analysis method obtained in block 1204 may include an error,
The threshold value according to Otsu's discriminant analysis method is corrected based on the candidate value for threshold value processing obtained in block 1205.
That is, the threshold value obtained in block 1204 is compared with the candidate value obtained in block 1205, and
The candidate value of the block 1205 which is closest to the threshold value is set as a threshold value for separating the travel road region from the background region (block 1206). Next, the brightness image is binarized using the threshold value (block 1207).

The brightness image is composed of 512 × 512 pixels and is expressed as I (i, j). Here, i and j are integers that satisfy the conditional expression 0 ≦ i, j ≦ 511. Further, the threshold value obtained in the block 1206 is X1, the minimum feature value of the feature amount range to be divided into regions is the threshold value X0, and the maximum feature value is the threshold value X.
Let it be 2. The initial values of thresholds X0 and X2 are 0 and FF (hex). Here, the brightness image I
If (i, j) satisfies the following conditional expression, the memory M
Write FF (hex) to 1 (i, j) (block 1
208). X0 ≦ I (i, j) <X1 If the brightness image I (i, j) satisfies the following conditional expression, FF (hex) is written to the memory M2 (i, j) (block 1209). . X1 ≦ I (i, j) <X2 Next, the template image stored in the ROM is read (block 1210). In the template image, a template area is set so that the most stable travel path information can be obtained. This area setting is performed based on the depression angle, the angle of view, and the focal length of the color camera attached to the autonomous vehicle.
F (hex) is stored. Next, the degree of overlap between the read template image and the memories M1 and M2 is calculated (block 1211). That is, the logical product of the template image and each of the memories M1 and M2 is calculated, and the number of pixels whose logical product result is “1” is accumulated for each of the memories M1 and M2.

Next, the total number of pixels for each memory is
The ratio to the number of pixels in the template area is determined. It is assumed that the road information is included in the memory information where the ratio is 50% or more, and the image stored in the memory M1 or M2 where the ratio is 50% or more is repeatedly divided as follows. I do. Further, by selecting the memory based on this ratio, the image is divided into regions by the threshold value X1, and the division is also performed on the vistogram.

That is, the area stored in the memory M1 whose characteristic amount is within the range of X0 to X1, and the region whose characteristic amount is X1 to X2
And the area stored in the memory M2 within the range of. When the overlap between each of the memories M1 and M2 and the template image does not exceed 50%, the memory M3 storing the sum of the memories M1 and M2 is selected (block 1212), and based on the memory M3. The divided region is then performed.

For example, when the degree of overlap between the memory M1 and the template image is high, an area constituted by the feature values within the threshold values X0 to X1 is set as a road candidate area (block 1213). Then, the road candidate region is further divided into regions. The threshold value X1 'for this repetitive region division is one of the valley candidate values obtained in the block 1205, which is within the range of the threshold values X0 to X1 (block 1214). If there is no valley candidate value within this range, the repetitive division processing is not performed.

The minimum value X0 'of the feature amount range to be subjected to the repeated region division is a threshold value X0 which is the same as the previous region division, and the maximum value X2' is the threshold value X1. The region constituted by the feature amounts within this range is re-binarized in block 1207, and thereafter, the same processing as the previous region division is performed, thereby repeatedly dividing the region. This repetition is performed until there are no more valley candidate values obtained in block 1205 within the range of the feature amount of the road candidate area to be divided. By repeatedly executing the processing as described above, the first road image area is finally obtained.

If there are shadow areas darker than lightness and bright high-luminance areas in the obtained road image area, thresholds for obtaining these areas are set as described later (block 1215). ). Then, based on the threshold value, each region is divided into regions in the same manner as described above, and a shadow region and a high luminance region are obtained. If the saturation of the obtained shadow region and the saturation of the road image region are similar according to the result of the processing based on the saturation image performed at the same time, the logical product of each of them is calculated, and the final value is determined as one region. Low brightness area. Similarly, when the calculated saturation of the high-luminance area and the saturation of the road image area are similar, the logical product of each of them is calculated and one area is set as the final high-luminance area.
Further, the connection relationship between the road image region and the low-luminance region is checked, and if there is a relationship that can be combined, a merging process is performed. Similarly, the connection relationship between the road image area and the high brightness area is checked.

When there is such a relation that merging is possible, merging processing is performed. As a result, when there is a shadow on the traveling road or when the position of the vehicle itself is in the shadow and direct sunlight is radiated to the far side of the traveling road and there is a high luminance portion, by performing this merging process, the actual traveling road is obtained. A travel path region having a suitable shape can be obtained.

Although the above processing is for a lightness image, the same processing is performed for a saturation image. However, since the processing in block 1215 is specific to the brightness image, this processing is not executed. As a result of the processing using the saturation image, in the case of similar colors in which R, G, and B of each area have similar values, the histogram distribution of each area has a similar shape. This is based on the above-described conversion formula from RGB data to saturation S data (S = 1−min (r, g, b) / 3)
Is understood from.

Therefore, it is difficult to discriminate the respective regions based on the saturation in the sky on cloudy weather or a general pavement road in which the values of R, G, and B are similar. However, when there is a color difference and the R, G, and B values of each area are not similar, each area can be distinguished even if there is no difference in brightness of each area. For this reason, in order to accurately extract the road image area under any scene, two feature amounts of lightness and saturation are used. Then, by taking the logical product of the two types of road images extracted from the brightness image and the saturation image, it is possible to obtain highly-accurate traveling road area information.

FIG. 13 shows a road candidate area extraction process in the road image area extraction processing. First, a feature amount histogram 1302 is created from the brightness feature amount image 1301. The horizontal axis of the histogram 1302 indicates lightness, and the lightness is represented by a numerical value from 0 to FF (hex). The vertical axis indicates the number of pixels in the original image at each brightness. This histogram 13
02, a threshold value c for distinguishing the road area from the background area is obtained. Further, based on the shape of the histogram 1302, a peak and a valley of the histogram are obtained, and a peak / valley table is created. Then, the obtained valley is set as a candidate value of the threshold value for the repeated threshold value process. In this histogram 1302, the feature values a, b, d, e, and f are candidate values.

The threshold value c obtained by the Otsu's discriminant analysis method is corrected by the candidate value obtained from the peak / valley table. That is, the threshold value c closest to the threshold value c is set as the corrected threshold value. Then, the feature amount image 1301 is binarized using the threshold value b. As a result, a divided image 1303 having a feature value of 00 (hex) to b and a feature value of b
To FF (hex) divided image 1304 are obtained. At this point, it is not known which of the divided images 1303 and 1304 contains the travel path area.

For this purpose, the logical product of the template image 1305 assuming the road position and each of the images 1303 and 1304 is calculated, and the degree of overlap with each image is calculated. Below the template image 1305, a trapezoidal template area is set as shown. As described above, FF (hex) is stored in the memory element corresponding to the template area, and 00 (hex) is stored in the memory element corresponding to the background area of the template area.
In the case of this example, the template image 13
05 overlaps the most. For this reason, based on the calculation result of the degree of overlap, it is determined that the image 1304 having the feature amount of b to FF (hex) includes the travel road area, and the road candidate area image 1306 corresponding to the image 1304
Is obtained.

Since other catch values still remain between the feature values b to FF (hex), the road candidate region image 1306 is further divided into regions. That is,
The road candidate area image 1306 is binarized by the threshold value d.
By this binarization, the divided images 1307 having the feature amounts b to d are obtained.
And a divided image 1308 having a feature value of d to FF (hex). Next, the obtained images 1307 and 1308
For the template image 1305 in the same manner as described above.
Calculate the degree of overlap with. In the case of this example, the image 1308
Since the degree of overlap with the template image 1305 is higher, it is determined that the image 1308 having the feature amount of d to FF (hex) includes the travel road area, and the road candidate area image 1309 corresponding to the image 1308 Is obtained.

Since another candidate value e still remains between the feature values d to FF (hex), the road candidate area image 13
09 is binarized by the threshold value e. As a result of this binarization, a divided image 1310 whose feature value is d to e and a feature value of e
To FF (hex). Then, the degree of overlap between each of the images 1310 and 1311 and the template image 1305 is calculated in the same manner as described above. In the case of this example, the image 1310 is the template image 130
5 has a high degree of overlap with the image 131, and the image 131 having the feature amounts d to e
It is determined that the travel path area is included in 0, and the image 1
A road candidate area 1312 corresponding to 310 is obtained.

Since no other candidate values remain between the feature amounts d to e, this road candidate area 1312 becomes the final binary image of the road area. The above processing is performed on the brightness image, but the same processing is performed on the saturation image. Thereafter, a logical product of the traveling road region extracted from the brightness image and the traveling road region extracted from the saturation image is obtained to obtain a final traveling road region.

A method of binarizing an image using a threshold and dividing the image into regions is general. However, as in the present embodiment, by calculating the degree of overlap with the divided image using the template image considering the position of the traveling road, the threshold value for the next region division performed on the histogram is set. The determination process can be performed quickly and simply. As a result, a travel path area that matches the actual travel path can be obtained at high speed, easily, and at low cost.

Further, the brightness of a scene captured by a camera may change as follows. For example, there may be a case where the brightness changes due to a change in the traveling direction of the own vehicle due to a curve, and a case where the brightness changes due to a clear or cloudy weather. In such a case, since the feature amount histogram does not always show a fixed shape, a correct travel road region cannot be obtained by region division using a fixed threshold value.
However, in this method, the state of the input image, that is,
Even if the brightness changes every moment, it is possible to always accurately extract the traveling road area. This is because, in the present method, a feature amount distribution having the highest degree of overlap with the template image is found, and an optimal threshold value according to the feature amount at that time is set for an image that changes every moment.

Conventionally, in image processing for region division, an original image is divided into a plurality of regions, and the divided images are subjected to identification processing to verify the validity of the road. However, in the above-described method, in order to extract a target object (road), region division is performed by threshold processing. As described above, in the conventional method, the target is verified from the processing result, but in the present method, the processing approach is reversed in that the target is targeted from the beginning of the processing. For this reason, it is possible to efficiently extract the road area.

Next, a method of extracting a shadow or a high-luminance portion from a traveling course paying attention to a difference in brightness will be described. This method has already been described briefly in the description of the “running course extraction method by repeated threshold processing using a template image”, and this method will be described in detail below.

For the brightness image, the above-described “running course extraction method by repeated threshold processing using a template image” and “threshold setting method based on feature histogram shape in repeated threshold processing” are applied. As a result, a histogram in which the characteristic amounts of the traveling course are distributed is obtained. In this method, an area darker than a road area and an area lighter than a road area are obtained based on the histogram. Further, this method is processed in the color image processing apparatus.

FIG. 14 shows an outline of processing when the present method is applied to each case where various input images are captured. Case 1 is a case where only a traveling course having a uniform road surface condition is captured as an input image. In this case, the vehicle is located just before the curve of the road that curves to the right. The road candidate area extracted in this case 1 is
It has the same shape as the input image. This is because the input image has only a uniform traveling course, and therefore, the low luminance region and the high luminance region are not extracted by this method.

[0099] Case 2 is a case where an input image in which a shadow is partially formed on the road surface of the traveling course and a high-luminance portion is formed far away from the traveling course by reflected light or the like. In the example in this case, the road curves rightward and has a branch road that curves leftward just before the right curve. Your vehicle is located just before these curves. The road candidate area extracted in this case 2 is
The shape is such that a dark area where a shadow is formed and a bright area which is a high luminance area are excluded. In addition, a shadow portion formed on the front side of the road can be individually extracted by extracting a low-luminance area by the present method. In addition, a high-luminance area due to reflected light formed far away on the road can be individually extracted by extracting the high-luminance area.

Case 3 is a case where an input image in which a shadow of a tree is formed on the road surface of the running course is captured in a case of strong sunshine, and a day when a tree leaks on the road surface. In the example in this case, the own vehicle is located in front of a curve on a road that curves to the left. This case 3
Has the same shape as the shadow created by the day of tree leakage. This is because the degree of overlap of the shadow part with the template image becomes high due to strong sunlight. Therefore, a low-luminance region is not extracted by this method. Further, a distant road area and a part where a tree leaks out are extracted as high-luminance areas due to strong sunlight.

Case 4 is a case where a discolored portion is formed in a belt shape along the side zone of the road, for example, a case where the road is discolored due to pavement work or the like. In the example in this case, the own vehicle is located at a position where the vehicle travels by looking at the discolored portion formed on the road traveling in a straight line to the right. The road candidate area extracted in the case 4 has a shape excluding the discolored portion. In addition, since the discolored portion has higher brightness than the road region, the discolored portion is extracted as a high luminance region by this method. Further, since there is no shadow or the like on the road surface, a low luminance area is not extracted.

Next, taking the case 2 as an example, the details of the present method will be described below. FIG. 15 is a histogram created based on the input image captured in Case 2. The feature of this histogram is lightness,
The lightness is shown on the horizontal axis. The vertical axis indicates the number of pixels at each brightness.

The feature amount range of the portion A in the figure is a range including the travel road area, and the pixel corresponding to the travel course most in the above-described “running course extraction method by repeated threshold processing using a template image” is determined. It is the part that is included in the range. In the range A, the feature amount is from t1 to t2.
The region is divided using the feature amounts t1 and t2 as threshold values. A portion B located on the left side of the feature amount t1 forms one crest sandwiched between valleys, and is a feature amount distribution in a dark range where the brightness is lower than the portion A. This B
The feature amount range of the portion is from t3 to t1, and each of the feature amounts t3 and t1 is a threshold for region division. Further, the portion C on the right side of the feature amount t2 is a feature amount distribution in a bright range having a higher brightness than the portion A, and forms one mountain sandwiched between valleys like the portion B. This C
The feature amount range of the portion is from t2 to t4, and each of the feature amounts t2 and t4 is a threshold value for region division.

Although the B portion and the C portion shown in the figure form one peak, the feature amount distribution which does not form one peak in this manner is a significant area for image segmentation on the image. Are not recognized. For this reason,
Such a feature amount distribution is not selected as a target of region extraction because it is not a superior distribution. In the illustrated example, both the B portion and the C portion have a dominant distribution, but the present method is applied even if only one of the distributions is dominant.

The feature amount distribution of the portion A corresponds to the road candidate area of case 2 shown in FIG. The feature amount distribution of the portion B corresponds to a shadow region having lower brightness than the road candidate region, and the feature amount distribution of the portion C corresponds to a high brightness portion having higher brightness than the road candidate region. This method uses A
Each region corresponding to each feature amount distribution of the B portion and the C portion adjacent to the portion is extracted.

First, in order to extract a region corresponding to the B portion of the histogram, the input image whose brightness is a feature amount is binarized by threshold values t3 and t1. Further, in the above-described “running course extraction method based on repeated threshold processing using a template image”, a road region image is obtained based on an input image having saturation as a feature amount. This road area image is obtained by first dividing an original image into areas, and is an image in which a dark area such as a shadow or a high luminance area is included in the road area. 1 is stored in the memory element corresponding to the road area, and 0 is stored in the memory elements corresponding to the other background areas. In the above-mentioned binarized image, the brightness is t3.
1 is stored in the memory element corresponding to the pixel area from t to t1, and 0 is stored in the memory element corresponding to the other area.
Is stored. For this reason, by taking the logical product of the road area image and the binarized image, only dark areas such as, for example, shadows on the roadway area are individually extracted.

In order to extract an area corresponding to the portion C, the threshold value t2 is determined in the same manner as in the case of obtaining the dark area.
The brightness image is binarized according to and t4. Then, the high-intensity areas are individually extracted by calculating the logical product of the road area image extracted from the saturation image and the binarized image in the same manner as in the case of the extraction of the B portion.

Next, a description will be given of a method for determining threshold values t3 and t4 required for dividing the B portion and the C portion into regions. The peak / valley table shown in FIG. 10 (b) was obtained by the above-described "threshold setting means based on the shape of the feature amount histogram in the repetitive threshold processing", but the histogram shown in FIG. Similarly, a peak / valley table (not shown) is obtained. Each table value in this peak / valley table is expressed as pkti as in FIG. 10 (b). The subscript i changes in order from i = 0, 1, 2,... N, N−1 from the origin of the graph corresponding to each table value.

In the histogram shown in FIG. 15, the table value corresponding to the threshold value t1 is pktj. Then, in the peak / valley table, this table value pkt
Looking at each table value from i to the left, pktk (pktk <
0) is determined. A feature point with a negative table value is a point corresponding to the bottom of a valley. There is a negative pktk by pkt0, and pktj
If the distance to the feature point of ktj is smaller than the threshold value (pktj-pktk <threshold value), the table value p
A feature value corresponding to ktk is set as a threshold value t3. Also,
If there is no negative pktk by pkt0, or if the distance exceeds the threshold, it is assumed that there is no dominant area darker than the road area.

The feature value corresponding to the threshold value t4 on the histogram can be obtained in the same manner as the method for obtaining the feature value corresponding to the threshold value t3. That is, the threshold value t
Assuming that a peak / valley table value corresponding to 2 is pktm, each table value is seen from the pktm to the right side.
It is determined whether there is a negative pktn (pktn <0) by pktN-1. pk that becomes negative by pktN-1
tn is present, and the distance from the feature point of pktn to the feature point of pktm is smaller than the threshold value (pktn
−pktm <threshold value), and a feature amount corresponding to the table value pktn is set as a threshold value t4. Also, pktN-1
If there is no negative pktn before, or if the distance exceeds the threshold position, it is assumed that there is no superior area brighter than the road area.

As described above, the present method uses the peak / valley table obtained by the “threshold setting means based on the shape of the feature amount histogram in the repetitive threshold value processing” to change the weather and the pavement. Even if the road surface condition changes due to road construction or the like, shadows, high-luminance portions, and discolored portions can be individually extracted.

Next, the "labeling device" will be described in detail below. The image extracted in the “method of extracting a traveling course by a repetitive threshold value process using a template image” has a finely divided region with noise. Therefore, each area of the extracted image is labeled by the labeling device, and the effectiveness of each labeled area is determined.
Based on the result of the labeling process, a region having a center of gravity higher than the horizon position and an unnecessary small region generated by noise or the like are removed.

FIG. 16 is a block diagram showing a schematic configuration of the labeling apparatus, and FIG. 17 is a general flowchart showing an outline of the labeling process. First,
The image extracted by the color processing device is transferred to an image bus (N
It works on the input memory 1601 via E BUS) (step 1701). This image information is 512 × 512
× 8-bit information, which is used as a multi-valued original image input. Next, primary labeling is performed using a temporary labeling method using a run described later (step 1702). This primary labeling is performed by a labeling processor (KLP) 1
602, a line buffer memory (LBM) 1603, a label matching memory (LMM) 1604, and the like.

After the primary labeling, preprocessing is performed to arrange the data arrangement in the label matching memory LMM 1604 (step 1703). After this preprocessing, secondary labeling is performed, and at the same time, feature amounts such as the area and the center of gravity of each region are extracted (step 1704). Step 1
Steps 703 and 1704 are mainly executed by the feature extraction processor KLC1605. By this secondary labeling, a label attached to a pixel located at each address is stored in a label memory (LABELM) 160
6 is stored. At the same time, the area and the center of gravity of each extracted region are stored in the feature memory 1607. After this, LA
The label image information stored in BELM 1606 is NE
Output to BUS (step 1705).

The number of KLPs 1602 used for label generation is one, and the LBM 1603, which is a line register for run processing, is configured using four label memories KLM, which will be described later. When the maximum number of temporary labels is 1023, the LMM 1604 uses eight KLMs. When the maximum number of temporary labels is 4095, KLM is set to 32.
The LMM 1604 is configured using the LMMs.

The conventional labeling is performed only when the input image is a binary image. However, the labeling according to the present method can perform the labeling for a multi-valued image by using the KLP1602. That is, it is possible to label several types of images at once. For example, assume that three types of binary input images 1801, 1802, and 1803 are input as shown in FIG. These binary images are added and converted to a multivalued image 1804 of 512 × 512 × 8 bits. This conversion processing is performed as preprocessing of the labeling processing. Labeling processor 1807 (KLP16) using line register 1805 (LBM1603) and label matching memory 1806 (LMM1604)
By the control of 02), the multi-value input image 1804 is labeled. In this labeling, the labeling of each area is arranged and finally output as a label image 1808 of 512 × 512 × 12 bits. By using the labeling processor 1807 (KLP1602), it becomes possible to perform labeling processing on a multi-valued image, reduce the number of temporary labels using runs, and perform one-scan labeling.

Each pixel of the multi-valued input image is labeled according to each pixel value under the control of the KLP 1602. Then, area division is performed based on the connection relationship between pixels having the same label value, and a new label is generated based on this connection relationship. For example, conventionally, in the case where an area composed of stepped pixels shown in FIG. 19A is formed in an input image, temporary labeling is performed on each pixel in the order of raster scanning, and each pixel is again labeled. The label generation has been performed by scanning for. As a result, at the time of provisional labeling, as shown in FIG.
Kind of temporary label was needed.

However, according to the labeling method using runs according to the present method, even if there is an input image shown in FIG. 19B composed of step-like pixels similar to FIG. By using this, the number of temporary labels can be reduced. That is, as shown in FIG. 3C, each pixel is divided into one row, ie, a run along raster scanning. In the case shown in the figure, it is divided into two runs 1 and 2. The labeling processor KLP writes flying in the line buffer memory LBM until the last scan of the run reaches the last pixel, determines a temporary label, and connects all pixels of the run to all the pixels of the run at the last pixel of the row. Is written in consideration of the temporary label.

As a result of scanning the run 1 shown in FIG. 11C and performing the above-described processing, the pixels corresponding to the run 1 are subjected to the temporary labeling shown in FIG. The labeling of the temporary label “1” is performed simultaneously for each pixel. This is because a labeling memory KLM, which will be described in detail later, is used as the memory. Followed by Run 2
, The temporary labeling shown in FIG. Since Run 2 is connected to the temporary label “1” of Run 1, the temporary label “1” is simultaneously written to each pixel of Run 2 when the last pixel of Run 2 is scanned. By the labeling using the run as described above, the labeling to the step-like pixels shown in FIG. 9B can be performed by one kind of label “1”, and the number of temporary labels is reduced. In other words, the pixels are grouped into one unit called a run, and the processing is performed in units of a run, whereby the size of the labeling circuit can be reduced.

Next, the details of the labeling process by the labeling processor KLP will be described. Labeling is performed by the window shown in FIG. 20 scanning each pixel along each run. By scanning this window along each run, a pixel of interest appears in the T (target) portion, a pixel located above the T portion appears in the a portion, and a pixel located on the right side of the T portion appears in the b portion. The next neighboring pixel appears. Hereinafter, T, a, and b indicate the label values of the input image appearing in each part. Note that when the window is scanning during the run, the value of the flag is output to the line buffer memory LBM as an output label, and when the window reaches the last pixel of the run, The label is written.

The internal structure of the KLP is shown in the block diagram of FIG. KLP includes a selector 2101, a temporary label register Tml2102, a counter Cnt2103,
First comparison circuit 2104 and second comparison circuit 2105
It consists of. The first comparison circuit 2104 is provided with the label values T, a, and b, and performs a multi-value comparison of the input image. The comparison result is given to the select terminal sel1 of the selector 2101. The second comparison circuit 2105 stores the value M in the label matching memory LMM of the label value a.
at (a), the value of the temporary label register Tml2102, and the value of the counter Cnt2103 are given, and at the same time, each value is given to the terminals A, B, C of the selector 2101. The value of Tml 2102 is determined by the output signal from selector 2101. The value of the flag FLAG stored in the line buffer memory LBM is given to the terminal D of the selector 2101.

The second comparison circuit 2105 compares these given values. The connection relationship between the labels is determined based on the comparison result, and the comparison result is output to the select terminal sel0 of the selector 2101. The selector 2101 determines a matching address MAT based on the given values.
It outputs ADDR and matching data MAT DATA, and changes the storage contents of the label matching memory LMM. At the same time, the selector 2101 outputs the value of the temporary label and the value of the output label (LABEL).

FIGS. 22 to 27 show flowcharts of window processing. FIG. 22 shows a first comparison circuit 210
4 is a flowchart illustrating a process of comparing and judging the values T, a, and b in FIG. First, the label value T of the pixel of interest is 0
Is determined (step 2201). If T is 0, processing 1 described below is executed (step 22).
02). If T is not 0, the label value T is compared with the label value b (step 2203). If the label value T is equal to the label value b, the label value T and the label value a
Are compared (step 2204). If the label value T is equal to the label value a, processing 2 is executed (step 2205). That is, the process 2 is a process executed when the values T, a, and b are equal. In this case, when each label value is expressed as ○, the window state is a state drawn adjacent to the illustrated processing box in step 2205.

If the label value T is not equal to the label value a, the processing 3 is executed (step 2206). That is, the process 3 is a process executed when the label value T is equal to the label value b and the label value T is different from the label value a. In this case, when the label value T and the label value b are represented by ○ and the label value a is represented by ×, the window state is a state drawn adjacent to the illustrated processing box in step 2206. Then, after the processing in step 2205 or step 2206, the line buffer memory LBM1
A flag is set at the position of the window T at 603 (step 2207).

In step 2203, if the label value T is not equal to the label value b, the label value T
Is compared with the label value a (step 2208). If the label value T is equal to the label value a, processing 4 is executed (step 2209). That is, the process 4 is performed with the label value T
And the label value a are equal, and the label value T is different from the label value b. In this case, when the label value T and the label value a are represented by ○ and the label value b is represented by ×, the window state is a state drawn adjacent to the illustrated processing box in step 2209.

If the label value T is not equal to the label value a, the processing 5 is executed (step 2210). That is, the process 5 is a process executed when the label value T is different from the label value a and the label value T is different from the label value b. In this case, if the label value T is represented by ○ and the label value a and the label value b are represented by ×, the window state is a state drawn adjacent to the illustrated processing box in step 2210. Then, after the processing of step 2209 or step 2210, the temporary label register Tml in the KLP is cleared (step 2211). This Tm
1 stores the label for the pixel in the T section immediately before reaching the current window position.

The flowcharts of FIGS. 23 to 27 described below show the flowcharts of the comparison determination processing from processing 1 to processing 5. FIG. 23 shows a flowchart of the above-mentioned processing 1. Processing 1 ends without executing anything. FIG. 24 shows a flowchart of the above-mentioned process 2.
First, the label value of the previous pixel stored in the temporary label register Tml is compared with 0 (step 2401). T
If the label value of ml is equal to 0, the value Mat of the label matching memory (LMM) 1604 of the label value a is
(A) is written into the temporary label register Tml2102 (step 2402). If the label value of Tml is not equal to 0, the label value of Tml and the counter Cnt21
03 is compared with the counter value (Step 2403).
The newest label value is stored in the counter 2103.

When the label value of the temporary label register Tml is equal to the counter value of the counter Cnt, the value Mat of the label matching memory (LMM) of the label value a is obtained.
(A) is written into Tml (step 2404). If the value of Tml is not equal to the value of the counter Cnt, the LMM value Mat (a) is compared with the value of Tml (step 2405). If the value of LMM Mat (a) is equal to the value of Tml, nothing is executed (step 2406). If the value of LMM Mat (a) is not equal to the value of Tml, the value of Tml and the value of LMM Ma
t (a), the smaller value ｛Min (Tml, M
at (a)) is written to Tml. Further, the smaller value of the two {Min (Tml, Mat (a))}
To a label matching memory [Mat @ Min (Tml, Mat
(A))} (step 2407). The value of the temporary label register Tml is compared with 0 (step 25).
01). If the value of Tml is equal to 0, the counter 2
Write the counter value of 103 to Tml (step 25)
02). Also, if the value of Tml is not equal to 0,
Nothing is executed (step 2503).

FIG. 26 is a diagram showing a flowchart of the above-mentioned process 4. First, the value of Tml is compared with 0 (step 2601). When the value of Tml is equal to 0, this Mat (a) is written to all the registers in which the flag is set with the T part, using the LMM value Mat (a) of the label value a as the label value of the target area ( Step 2602). At this time, all the LBM flags are cleared. If the value of Tml is not equal to 0 in step 2601, the value of Tml is compared with the value of Cnt (step 2603). When the value of Tml is equal to the value of Cnt, the value of the LMM Mat (a) of the label value a is set as the label value of the target area, and this register Mat (a) is stored in all the registers in which the T portion and the flag are set. Is written (step 2604). At this time, all the LBM flags are cleared.

If the value of Tml is not equal to the value of Cnt, the value of Tml is compared with the value of Mat (a) (step 2605). Then, the value of Tml and Ma
If the value of t (a) is equal, the LMM of label value a
The value Mat (a) is used as the label value of the target area, and the T portion and this register
At (a) is written (step 2606). At this time,
All the LBM flags are cleared. If the value of Tml is not equal to the value of Mat (a), the smaller value of the value of Tml and the value of LMM Mat (a) of the label value a ｛Min (Tml, Mat (a) )} As the label value of the target area, and writes it to the T section and all the registers in which the flags are set. At this time, all the LBM flags are cleared. Further, the value of Tml and the value of LMM Ma
t (a), the smaller value ｛Min (Tml, M
at (a))} is stored in a label matching memory [Mat @ M] of a label value equal to the larger value of the two.
in (Tml, Mat (a))}] (step 2607).

FIG. 27 is a diagram showing a flowchart of the above-mentioned process 5. First, the value of the temporary label register Tml is compared with 0 (step 2701). Tml value is 0
If the value of Cnt is equal to the value of Cnt, the value of Cnt is written to all the registers where the flag is set with the T part, using the value of Cnt as the label value of the target area. At this time, all the LBM flags are cleared. Further, the value of Cnt is changed to Cnt
To the label value Mat (Cnt) equal to the value of
Increment the value of Cnt by one (step 270)
2).

If the value of Tml is not equal to 0, the value of Tml is used as the label value of the target area.
The value of Tml is written into the T section and all the registers in which flags are set (step 2703). At this time, all the flags of the LBM are cleared. Next, the value of Tml and Cnt
(Step 2704). If the value of Tml is equal to the value of Cnt, the value of Cnt is
Is written to Mat (Cnt) of the label value equal to the value of.
Further, the value of Cnt is counted up by one (step 2705). If the value of Tml is not equal to the value of Cnt, nothing is executed (step 270).
6).

Next, the labeling memory KLM used for the line buffer memory LBM and the label matching memory LMM will be described. Until now, data could only be written to one internal register by one address specification. However, by using the KLM described below, data can be written to a plurality of internal registers at once. Therefore, the present labeling memory KLM uses a line register (LB) for run processing.
M), label matching memory (LM
M), label image memory for one scan (LABEL)
M) and a label matching memory capable of performing label matching.

The KLM is composed of a plurality of registers. FIG. 28 shows a block configuration of one of these registers. This block is KLM
Is one unit of the configuration. A comparator 2802 is connected to each register 2801 in pairs. This comparator 2802 is provided with output data DATA from register 2801 and information COM to be compared with the output data. Comparator 2802 compares the applied data and outputs the comparison result to AND circuit 2803. The output of the address decoder circuit 2804 is also supplied to the AND circuit 2803. The AND circuit 2803 outputs a signal to the OR circuit 2805 when either the comparator 2802 or the decoder circuit 2804 outputs a signal.

The OR circuit 2805 is supplied with a write signal WR from the CPU, and an enable signal is supplied to the register 2801 in synchronization with the write signal WR.
That is, when the address is selected by the address decoder circuit 2804 or the comparison result in the comparator circuit 2802 matches, the register 2 is synchronized with the write signal WR.
Data is written to 801. Each decoder circuit 280
4 and each comparator circuit 2802
Are all executed simultaneously. Therefore, by one addressing or one data comparison judgment,
It is possible to simultaneously rewrite the contents of a plurality of registers constituting the KLM.

The temporary label obtained by the labeling process using the run becomes a discontinuous value when the label integration is performed. The contents of the label matching memory LMM at this time are shown in FIG. Labels 1, 3, and 6 are stored as data corresponding to the pixels at addresses 1 to 10, respectively. Since this label value is a discontinuous value, it is converted into a label having a continuous value shown in FIG. 10C by the feature extraction processor KLC shown in FIG. 10B and described in detail later. That is, the label values stored in the label matching memory LMM constituted by the KLM are continuous values of 1, 2, and 3.

More specifically, the labeling processor KL
C generates the address of the LMM and takes in the data indicated by the address. Subsequently, the fetched data is compared with the address.
And rewrite the label value. If the values are different, the next address is output to the LMM and the next address is compared with the data. Thereafter, by repeating this process, the discontinuous label values shown in FIG. 29A are converted into the continuous label values shown in FIG. 29C.

More specifically, since the data (1) at address 1 in FIG. 14A is the same as the address, the KLC outputs 1 as new data and the same data (1) as the data at address 1 is output. The data at addresses 2, 4, 5, and 7 is rewritten to new data 1. In the case shown in the figure, the old data and the new data happen to be the same 1, so that there is no change in the data of the corresponding address in FIGS. Next, the data (1) at address 2 is compared with the address. Because addresses and data are different,
Generate the next address 3. Then, data (3) at address 3 is compared with the address. Since the address and the data are the same, 2 is output as new data.
Since the LMM is composed of the KLM, the addresses 9 and 10 having the same data (3) as the data of the address 3
Are all rewritten to 2 at the same time as shown in FIG. Next, the KLC generates a new address 4. Since the address and the data are different, the address 5 is generated next, and thereafter, the same processing as described above is repeated. As a result, discontinuous values are converted to continuous values.

FIG. 30 shows the feature extraction processor KLC.
FIG. 2 is a block diagram showing an internal configuration of the device. By using this KLC, pre-processing of a temporary label generated by primary labeling is performed. Also, at the time of secondary labeling,
At the same time as the labeling, the KLC calculates the area of the same label area, the sum of the X-direction addresses of the same label area, and the sum of the Y-direction addresses of the same label area.
Performed by

The KLC includes a +1 adder 3001, two adders 3002 and 3003, comparators 3004 and 2
And three counters 3005 and 3006. When there is a pixel input having the same label value, the +1 adder 3001 counts up the count number one by one, calculates the area of the same label area, and divides it by Siz.
Output as e. The X direction address value and the Y direction address value are input to the adders 3002 and 3003. Then, when there is a pixel input of the same label value, the address value is added for each direction, and the sum of the address values of the same label area is calculated for each address direction. Each sum is output as X ADDR and YA ADDR. By dividing the total value of addresses in each direction by the area of the same label area, the center of gravity in each direction can be obtained. Then, the center of gravity of each identical label region is obtained, and the region having the center of gravity above the horizon is removed as not being an effective region for extracting a road candidate region.

The comparator 3004 and the counter 3005
Reference numeral 3006 is used for preprocessing of temporary labels generated at the time of primary labeling, that is, processing for converting discontinuous label values into continuous label values. Counters 3005 and 300
6, a clock signal CLK is input, and the counter 300
The output of 5 is the matching address MAT ADDR output to the label matching memory LMM. This address is also given to the comparator 3004 at the same time. The output of the counter 3006 is a label matching memory LMM
Becomes the data MAT DATA to be output. Comparator 3
004 compares the given address and data as described above, and outputs a signal to the counter 3006 when the respective values of the address and data match. Counter 30
When the signal 06 is input, the current counter value is output to Mat DATA, and the value is incremented by one.

The processing time of the labeling process described above requires the sum of the raster scan time of two scans performed along each run and the label integration time. However, the label memory LABELM1 shown in FIG.
By using the labeling memory KLM described above at 606, the labeling process can be executed in one scan. That is, as shown in FIG. 31, when a multi-valued input image 3101 is captured, the labeling processor KLP 3102
A labeling process is executed using the label 103 and the label matching memory 3104 in the same manner as described above. The label value of each pixel obtained by this labeling is stored in the label image memory 310 at the time of scanning of the last pixel of each run.
5 is written as it is. Since label integration is not performed, the value of each label is stored as a discontinuous value.

As described above, in the labeling memory KLM, comparators are connected to each of the internal registers in pairs, and new data is simultaneously stored in the register whose comparison result matches and the register whose address is selected. What is written. One-scan labeling is enabled by the characteristics of the KLM.
The labeling processor KLP is a hardware version of the algorithm of the present system, and enables high-speed labeling.

With this one-scan labeling method, it is possible to reduce the labeling processing time to half or less than before. For example, if the device is operating with a clock signal of 8 MHz, the labeling processing time up to now may be 66 msec for scanning two windows.
And the time required for label integration. However, according to this one-scan method, 33 or less of 1/2 is used.
The labeling process can be performed in msec.

[0145] In addition, normal pixel scanning is performed from the upper left to the lower right of the screen of the input image. In the image processing for extracting the road area, the target image is located below the image. Therefore, there is a possibility that the memory for storing the temporary label overflows when the target image is scanned. For this reason, scanning is performed from the lower right to the upper left of the screen, and the target image is scanned first so that the target image can always be obtained. In other words, if the target image is obtained first, even if a memory overflow occurs, the image scanning at the time of the overflow becomes a scan of an unnecessary image portion, and the target image can always be obtained. .

Next, the "method of merging a plurality of regions" obtained by dividing an image will be described. A road candidate area is obtained by "a method of extracting a traveling course by repeated threshold processing using a template image", and a low-luminance area is extracted by "an extraction means of a shadow or a high-luminance portion on the traveling course focusing on a difference in brightness". Shadows and high-luminance areas. Based on this road candidate area, a low-luminance area and a high-luminance area strongly connected to the road candidate area are merged into a road candidate area, treated as one area, and regarded as a traveling course. For this purpose, the common boundary length adjacent to each area and the perimeter of each area are determined, and the relationship between the connected areas is described. The description of the relationship between the regions indicates the merging relationship, and the same result as that of the merging process of the regions can be obtained without performing the label change operation as in the related art. Note that the common boundary length is determined based on the length of the side of the pixel adjacent to each area, and the perimeter is determined based on the length of the side of the outermost pixel of each area.

The algorithm of the present method will be described below. There are two types of this method. First, there is an inverted L-shaped mask scanning method for scanning an inverted L-shaped mask. This method is a simple algorithm and is suitable for hardware implementation. Secondly, there is an area boundary exploration method for locally exploring the boundary of the area.
Since this method searches only the boundary of a necessary area, it can be processed with a small memory and is effective.

The first method, the inverted L-shaped mask scanning method, is executed by scanning the inverted L-shaped mask shown in FIG. 32 from left to right and from top to bottom of the label image.
The x pixel appearing in the illustrated mask is the pixel of interest, and a
The pixel is a pixel located above the pixel of interest x, and the pixel b is a pixel located to the left of the pixel of interest x. For example, assume the pixel area shown in FIG. The mouth shown in the figure represents one pixel, and the numerical value in this mouth represents the label value of that pixel. In the case of this example, the area of the label value 1 and the area of the label value 4 are adjacent to each other. Although not shown in the figure, the label value of the background area is 0.

The perimeter length ob of each area is obtained by scanning the inverted L-shaped mask along the row of each area, that is, along the run. Specifically, the perimeter ob1 of the area of the label value 1 is obtained as follows. First, the x pixel of the inverted L-shaped mask is matched with the pixel located at the left end of the uppermost run. In this case, the pixel a and the pixel b are in the background area and the label value is 0, so that two sides of this pixel have the label value 1
It can be seen that it is located around the region of. Therefore, 2 is added to the counter that counts the circumference length ob1. Next, the inverted L-shaped mask is scanned to the right, and the x pixel is adjusted to the right neighboring pixel. In this case, the pixel a is located in the background area, and the pixel b is located in a pixel in the main area having a label value of 1 with the previous mask. Therefore, it can be seen that one side of this pixel is located around the main area having the label value of 1, and the counter further stores 1
Add. By scanning the area of the label value 1 along the run with the inverted L-shaped mask in this manner, the circumference length ob
1 becomes 18. The unit of the perimeter ob is the number of sides of the pixel.

The perimeter ob4 of the area of the label value 4 is also obtained in the same manner as the above perimeter ob1, and its value is 20. Further, the common boundary length cb between the area of the label value 1 and the area of the label value 4 can be obtained as follows. This common boundary length cb is different between a common boundary length cb14 with the label value 4 area as viewed from the label value 1 area and a common boundary length cb41 with the label value 1 area as viewed from the label value 4 area. There are cases. In other words, the mask has an inverted L-shape, and only two neighboring pixels of each pixel are taken into account, causing a difference in the common boundary length. However, there is no problem in the merging processing of a plurality of regions. It should be noted that if the mask to be scanned has a cross shape that looks at the vicinity of 4 of the pixel of interest, there is no difference in the common boundary length.

The common boundary length cb14 with respect to the label value 4 as viewed from the area of the label value 1 is obtained by mask scanning when obtaining the peripheral length obl. This common boundary length cb14 becomes zero. That is, even if the inverse L-shaped mask is scanned for each run in the area of the label value 1, the pixel of the label value 4 does not appear in the a pixel and the b pixel.

The common boundary length cb41 from the label value 4 region to the label value 1 region can be obtained by mask scanning when obtaining the peripheral length ob4. This common boundary length cb41 is 3. That is, when the mask is located at the first pixel of the second and third runs in the area with the label value 4, the pixel with the label value 1 appears at the b pixel. For this reason, the mask scan for the run up to the third stage
The value of the counter that counts the common boundary length cb41 is 2. Further, when the mask is located at the head position of the fourth run, a pixel having a label value of 1 appears at the a pixel of the mask. Therefore, 1 is added to the counter, the integrated value of the counter becomes 3, and the common boundary length cb41 becomes 3. The unit of the common boundary length cb is the number of sides of the pixel.

Next, the values of the common boundary length between the regions and the perimeter of each region thus determined are stored in the inter-label boundary length matrix shown in FIG. The numbers 0, 1, 2,..., J,.
k, and the numbers 0, 1, 2,..., i,.
l indicates a label value. The numerical value mij stored at the location designated by the numerical value of each column and each row is the common boundary length with the region of the label value j as viewed from the region of the label value i. The numerical value ti stored in the (k + 1) -th column is the perimeter of the area of the label value i.

Taking the pixel area shown in FIG. 33 as an example, the value of the common boundary length cb14 with the area of label value 4 as viewed from the area of label value 1 is 0. 0 is stored in the memory located in the column. Also,
Since the value of the common boundary length cb41 with respect to the area of the label value 1 as viewed from the area of the label value 4 is 3, 3 is stored in the memory located at the 4th row and the 1st column of the same matrix.

After the matrix is created by obtaining the perimeter of each area and the common boundary length between the areas, the connectivity between the areas is calculated. This connection degree is obtained by the following equation based on the common boundary length and the perimeter. Degree of connection = X / min (A, B) where A is the perimeter of the area of the label value i, B is the perimeter of the area of the label value j, and min (A, B) is each of A and B Represents the smaller of the numbers. X is the average value of the common boundary length between the respective areas represented by the following equation. X = (X1 + X2) / 2 X1 is the common border length with the label value j area viewed from the label value i area, and X2 is the common border length with the label value i area viewed from the label value j area. . The above expression for X is
This is effective when both X1 and X2 are not 0 (X1 ≠ 0, X2 ≠ 0). When X2 is 0 (X2 = 0), X is represented by the following equation. X = X1 When X1 is 0 (X1 = 0), X is represented by the following equation. X = X2 If the calculated connectivity is equal to or greater than a predetermined threshold, the connection between the area of the label value i and the area of the label value j is strong, and the areas are in a relationship to be merged. If the degree of connection is equal to or less than the predetermined threshold value, the connection between the area of the label value i and the area of the label value j is weak, and the areas do not have to be merged. This merging relationship is described in the connection label column of the feature amount list shown in FIG.

Label No. of the list Is the label value, and the area and the center of gravity are the area and the center of gravity of the area of the label value. The area and the center of gravity are obtained at the time of the labeling process described above. The circumscribed rectangle is a rectangle that surrounds all the pixels in the area of the label value. The circumscribed rectangle is the coordinate t of the upper left vertex of the four vertices that are the intersections of the sides.
L (x, y) and the coordinates bR (x, y) of the lower right vertex are specified. Each coordinate is obtained in the above-described boundary search process. In addition, the label No. described in the connection label column. Indicates the label value of the area that is to be merged with the area of the label value. When there is an area to be further merged with the area described in this column, as shown in the figure, the connection labels are further connected by a pointer.

Each feature described in the list is for each area shown in FIG. Label values 11 to 14 are assigned to the respective regions, and G1 to G4 indicate the positions of the centers of gravity of the respective regions. A circumscribed rectangle is drawn in each area. The contact point between the upper side of this rectangle and the area is represented as coordinates st (x, y), and the area start point tL
It is obtained by performing a horizontal operation from (x, y). Coordinates s
t is used in a method described later.

Since the area of the label value 11 and the area of the label value 12 are to be merged, the label NO. The label value l2 is described in the connection label column of the line l1. Further, the area of the label value 11 is the label value 13
And the region of label value l4, the connection labels of label values l3 and l4 are linked by a pointer. In addition, the label No. l2 to l4
The connection relationship between the areas is described in the connection label column of each line in the same way.

Next, an area boundary search method will be described. First, a rectangle circumscribing the target area is drawn, and a search start pixel located at coordinates st (x, y) where the circumscribed rectangle and the area are in contact is determined. Then, the pixels located in the vicinity of 8 of the search start pixel are examined. Each reference pixel located in the vicinity of No. 8 has a position No. 8 as shown in FIG. Is attached. That is, N located above the pixel of interest
o. Is set to 0, and numbers are sequentially assigned in the clockwise direction. The coordinates of the pixel to be searched next to the search start pixel are shown in FIG.
The determination is made using the pixel reference table shown in FIG.
The table shown in FIG. 3A is a table that is referred to when a search for a pixel is executed clockwise (clockwise).
The table shown in FIG. 6B is a table that is referred to when a search for a pixel is executed in a counterclockwise direction (counterclockwise). Numerical values of 0 to 7 between brackets ｛｝ in the table correspond to the position No. of the reference pixel in FIG. It corresponds to.

The next pixel to be searched is the reference pixel position No. corresponding to the current pixel position with respect to the previous pixel position. Is obtained, and this position No. Row No. of the table that matches Column No. Is determined by referring to. A method for determining a search pixel using the pixel reference table will be specifically described below. Assume that the boundary of the pixel area is formed as shown in FIG. The shaded mouths in the figure indicate pixels whose label value is not 0. The previously searched pixel is indicated by the symbol △, the pixel currently searched for is indicated by the symbol 、, and the pixel to be searched next is indicated by the symbol ◎.

The current pixel position △ with respect to the previous pixel position △ is the reference pixel position No. in FIG. Corresponds to 1. If a pixel near the boundary of the area is searched clockwise, the pixel reference table refers to the table in FIG. 38 (a). Reference pixel position No. Is 1, the row No. Is the column No. of 1 $ 7,0,1,2,3
4, 5, 6}. Note that row No. Is 0 in the top row and is 1 to 7 in order. In addition, column No.
Is the number of the pixel to be referred to. Are all described. By referring to the eight neighborhoods of the pixel in this order, it is possible to avoid the modulo calculation in the normal address calculation.

That is, the column No. Starts from 7, the next pixel to be searched is a pixel ◎ located diagonally upper left of the current position ○. Pixel ◎ is a background area, and the label value is 0. To search for the next pixel. That is, the next reference pixel position No. Is 0, so
Search for a pixel located above the current position ○. Since this pixel is also in the background area, the column No. According to the reference pixel position No. Searches for one pixel. Since this pixel has a label value and is not 0, the reference of the next pixel to be searched is determined by this position No. In one pixel, a search is made for these eight neighborhoods in the same manner as described above. Then, the above processing is performed for each pixel along the boundary of the area, and the same processing is repeated for each pixel until the pixel returns to the search start pixel.

In this search processing, the processing according to the following rules is performed to obtain the perimeter length obi of the own area of the label value i and the common boundary length cbij between the adjacent area of the label value j. I can do it. In other words, if the label value j exists in an area adjacent to the vicinity of the upper, lower, left, and right sides of the current position, 1 is added to the counter that counts the common boundary length cbij. At the same time, it adds 1 to a counter that counts its own perimeter obi. If there is no pixel having the same label value as its own label value i in the vicinity of 4 at the current position, 1 is added to the counter of its own perimeter.

Next, based on the determined perimeter and common boundary length, the degree of connection between the respective regions is obtained in the same manner as in the calculation using the inverted L-shaped mask operation method. At the same time, a feature amount list is created in the same manner as described above. The connection label between the regions is described in the connection label of the feature amount list, and the merging relationship of a plurality of regions is determined. That is, even with this method, the merging relationship of each area can be determined without performing a label change operation of each area. Although the above boundary search was performed clockwise, if the boundary search is performed counterclockwise,
By using the counterclockwise pixel reference table shown in FIG. 38 (b), it is possible to perform processing in the same manner as clockwise.

By using the pixel reference table as described above, a fast boundary search can be performed. In other words, it is not necessary to check all the pixels in the vicinity of the pixel 8 serving as the reference for the search. Further, only the pixels near the boundary of the region need to be searched, and it is not necessary to search for all the pixels in the region. Therefore, processing time is reduced.

According to each of the methods described above, the merging relationship between the divided areas can be determined without performing the label replacement scan as in the related art. In other words, the road candidate area obtained by the “extraction method of the traveling course by the repetitive threshold value processing using the template image” and the “extraction means of the shadow and the high luminance portion on the traveling course focusing on the difference in brightness” are used. The obtained low-brightness and high-brightness areas are merged, and a real-world running course area can be obtained.

Next, the "means for obtaining the travelable range" will be described. This means is based on “a method of extracting a running course by repeated threshold processing using template images”, “threshold setting means based on the shape of the feature histogram in repeated threshold processing”, and “focusing on differences in brightness. The final travelable range, that is, road end coordinates, is obtained from the traveling course image obtained by the “extraction means for shadows and high-luminance portions on the traveling course”.

FIG. 40 is a flow chart showing the overall processing flow of the traveling course recognition system according to this method.
First, image information of a traveling course is captured by a color camera input device, and the captured R, G, and B images are converted into brightness I and saturation S images as before. Based on each of the converted images, the color processing apparatus creates a histogram using the lightness I as a feature and a histogram using the saturation S as a feature (step 400).
1). Next, based on each of the created histograms, a road candidate area is extracted by a “running course extraction method using repeated threshold processing using a template image”, and small areas are removed (step 4002).

Next, according to this method, a sequence of points at the road end of the extracted road candidate area is evaluated (step 4003). Then, based on the evaluation result of the point sequence at the road end, it is determined whether or not the extracted road candidate area is a monotone road (step 40).
04). If the road is a monotonous road, the relation between the obtained plurality of point sequences is further compared and evaluated (step 4005). Then, the evaluation result is finally output (step 4006). Thereafter, it is assumed that a dot sequence for the image input this time has been obtained, and the process for the next image is executed.

If the result of the determination in step 4004 is that the road-likeness is low and the road is not a monotonous road, the means for extracting shadows and high-brightness parts on the traveling course focusing on the difference in brightness is used. If there is, this is extracted (step 4007). Then, the obtained low-luminance area and the road candidate area are merged, and a point sequence at the road end is evaluated by this method (step 4008). Based on the evaluation result of the point sequence at the road end, it is determined whether or not the discharged road candidate area is a monotone road (step 4009). If the road is monotonous,
The processing after step 4005 is executed, and the processing shifts to processing for the next image.

If the road is not a monotonous road, then, if there is a high-luminance area, if there is a high-luminance area, it is extracted by "extraction means for a shadow or a high-luminance portion on the traveling course paying attention to the difference in brightness" ( Step 4010). Then, the obtained high-luminance area and the road candidate area are merged, and the point sequence at the road end is evaluated by this method (step 4011). Based on the evaluation result of the point sequence at the road end, it is determined whether or not the extracted road candidate area is a monotone road (step 4012). If the road is a monotone road, the processing after step 4005 is executed, and the processing shifts to the processing for the next image. If the road is not a monotone road, the low-brightness region and the high-brightness region are merged into the road candidate region, and a point sequence at the road end obtained from the region into which the three regions are merged is evaluated (step 4013). After that, the processing after step 4005 is executed, and the processing shifts to the processing for the next image.

Next, a method of obtaining the point sequence coordinates of the road edge of the road candidate area will be described below. For example, assume that the image area shown in FIG. 41 is obtained.
The origin is at the upper left of the figure, the x coordinate is positive in the horizontal direction to the right, and the y coordinate is positive in the vertical direction downward. There are cuts in the road area shown in this image, and places where image information could not be obtained due to noise or the like are scattered. It is assumed that a labeling process is performed on each pixel of the image shown in FIG. 42, and a pixel region obtained as a result of this process is as shown in FIG. The hatched portion in the figure is a pixel having a certain label value instead of a label value of zero. Note that pixels without oblique lines are pixels located in the background area where the label value is 0. Also, a frame 4201 surrounding 3 × 3 pixels
Is a window W, and the number of oblique pixels (pixels having pixel values other than 0) existing in the window W is a histogram value. The value of the histogram based on the window W in the case of the position shown in FIG. Although the illustrated window W is a 3 × 3 window, it may be a window of another size such as 5 × 5.

First, the window W is horizontally scanned from the center of the image to the left. The window W is scanned from the center of the image only at the start of the processing. The histogram value of the window W at each position is obtained while moving. When the window W is moved while monitoring the histogram value and a histogram value equal to or less than a predetermined threshold value is continuously obtained, that is, when the density of the hatched pixels in the window W decreases, the window W It is determined that the vehicle is out of the road area. Then, the position of the first window W where the histogram value has become equal to or less than the predetermined threshold value is set as the point sequence coordinates XL of the left road end. Next, the window W is horizontally scanned to the right, and when the histogram value in the window W continuously falls below a predetermined threshold value, the first window W in which the histogram value changes
Is set as the point sequence coordinate XR of the right road end.

Next, the Y coordinate at which the window W is located is set to 1
The horizontal scanning position is shifted by moving the window W upward of the image. Then, the center position of the road area is obtained from the road end coordinates XL and XR obtained by the first horizontal scanning by calculating Expression (XR-XL) / 2. This center position is set as the scanning start position of the window W,
The window W is scanned right and left. Also in this scanning, the histogram value in the window W is monitored in the same manner as described above, and the position where the histogram value changes is set as the coordinates of the road edge. Hereinafter, by repeating the same processing until the Y coordinate of the horizontal scanning position becomes the horizon position, a series of point sequence coordinates of the left and right road edges can be obtained.

In scanning the window W,
When the histogram value obtained from the window W at the scanning start position is equal to or less than the predetermined threshold value from the beginning, and the histogram value obtained by scanning the window W to the left is continuously equal to or less than the predetermined threshold value. , The center position becomes the road end coordinates. However, in this case, the scanning direction of the window W is changed to the opposite right side. When the histogram values equal to or more than the predetermined threshold value are continuously obtained, the position of the window W where the histogram value equal to or more than the predetermined threshold value is obtained first is set as the coordinates of the left road edge. .
The coordinates of the road edge on the right side can be found by scanning the window W further rightward and finding the position where the histogram value changes.

Next, since the point sequence coordinates of the road end obtained in this way exist near the boundary of the area, the boundary line obtained by connecting the points is not uniformly smooth. For this reason, the angles formed by each point located before and after a certain point and this one point are calculated for all the points in the point sequence, and this variance value is used as a reference for smoothness. When the obtained angle is an acute angle, the point is removed for calculation. After smoothing the point sequence coordinates of the left and right road edges in this way, these point sequence coordinates are projectively transformed into the real space. This projective transformation is performed based on the depression angle, height, and focal length of the color camera attached to the autonomous vehicle.

Furthermore, in the point sequence after the projective transformation,
The distance between the left and right point sequences, that is, the road width, is determined using the left and right point sequences as a set. Then, the number of points where the road width becomes narrower than the vehicle body width of the traveling vehicle is counted, and the points of this narrow width with respect to all points in the point sequence are calculated. When the ratio of the points of the narrow width to the total number of points is small, the points of the narrow width are not suitable as the road end points, and thus these points are removed.

When evaluating the point sequence at the road end,
Count the left and right pairs that have a small difference from the road width determined by the pair of left and right edges determined first. If the ratio of the left and right pairs with a small difference to the total pair is large, that is, if the left and right road end rows are parallel to some extent, the evaluation as road-likeness is the highest, and it is determined as a monotone road. Is done.
When it is determined that the road is a monotonous road, steps 4003 and 400 of the flowchart shown in FIG.
The road edge can be obtained without performing the processing of 8,4011.

Next, a description will be given of a means for correcting the point sequence at the left and right ends of the road when the autonomous vehicle is extremely close to the road edge. FIGS. 43 (a) to 43 (d) show a process in which an autonomous vehicle approaches a road edge. FIG. 3A shows a case where the autonomous traveling vehicle is located just before a sharp curve. In this case, the left road end L and the right road end R are captured in the field of view by the color camera attached to the autonomous vehicle. When the curvature of the road curve is steep, the autonomous vehicle located in FIG. 11A moves to the position shown in FIG. In this case, the right road edge R disappears from the field of view of the camera.

Further, the autonomous vehicle moves to the position shown in FIG. 17C, and the ratio of the area outside the road in the template gradually increases. The shaded area in the figure indicates the area outside the road. Further, the autonomous vehicle moves to the position shown in FIG. In this case, the left side of the road edge on the left side and the left side boundary of the area outside the road are captured in the field of view of the camera.
In addition, the ratio of the area outside the road to the template image increases, and the area outside the road is mistaken for the traveling course. As a result, the left road edge is erroneously recognized as the right road edge R, and the left boundary of the area outside the road is erroneously recognized as the left road edge L.

However, the point sequence at the road end obtained from the sequentially captured image information does not become a point sequence in which the right point sequence in the previous image is located extremely leftward in the current image. Conversely, the point sequence on the left side in the previous image does not become a point sequence extremely located on the right side in the current image. Therefore, the point sequence information obtained from the image information one processing cycle ago is stored and held, and the distance between the point sequence obtained from the current image information and the point sequence obtained from the previous image information will be described later. calculate. Then, for example, if the distance between the right-hand point sequence obtained at the last time and the right-hand point sequence obtained this time is far away, and the last right-point sequence is close to the current left-point sequence, It is determined that the positions of the left and right road edges have been reversed. Then, the right side and the left side of the road edge obtained this time are switched.

This will be described with reference to the image shown in FIG. The image in FIG. 11C is the image obtained last time, and the point sequence at the left side of the road is represented as Li-1. The image shown in FIG. 3D is the image obtained this time, and the point sequence on the left road edge that is misidentified is represented by Li, and the point sequence on the right road edge that is misidentified is represented by Ri. The determination as to whether or not the point sequence at the left and right road edges is reversed is made by determining which of the current point sequence Li and Ri is closer to the previous point sequence Li-1. When the previous point sequence Li-1 is close to the current point sequence Ri, it is determined that the right and left road end point sequences are reversed.

Generally, the closeness of the point sequence is determined as follows. That is, the left point sequence obtained from the current image is 1
The point sequence on the right side of the group is regarded as another group, and it is determined by judging which of the groups the previous point sequence on the left or right side is closer to. The determination as to which of these groups is closer is determined by Mahalanobis' generalized distance, which will be described in detail below. FIG. 44 is a diagram for explaining the Mahalanobis' generalized distance.

The point of the left point sequence Li-1 (or right point sequence Ri-1) based on the previous image information is (x, y), the point of the left point sequence Li based on the current image information is (x1j, y1j), The point of the right point sequence Ri based on the current image information is (x2j, y2j). The points (x, y) of the previous point sequence are the current point sequences Li and Ri.
The Mahalanobis' general distance is calculated for all points in the point sequence Li-1 (or the point sequence Ri-1) as follows, and the point to which many short-point points belong is determined. It is assumed to be close to a point sequence. When the point sequence obtained from the current image is a straight line, the distance to this straight line is calculated.

First, for each point on the point sequence Li, the average value μ11 of the x direction component, the average value μ12 of the y direction component, the variance value σ11 of the x direction component, the variance value σ12 of the y direction component, x, y The direction covariance value σ112 and the correlation coefficient ρ1 are obtained. Here, ni is the number of points located on the point sequence Li. In addition,
Each point on the point sequence Ri can also be obtained by the same equation as the following equation. μ11 = Σx1j / n1, μ12 = Σy1j / n1 σ11 ² = Σ (x1j-μ11) ² / (n1 -1) σ12 ² = Σ (y1j-μ12) ² / (n1 -1) σ112 = 112 (x1j-μ11 ) (Y1j-μ12) / (n1-
1) p1 = σ112 / σ11 × σ12 based on respective numbers, points of the previous sequence of points (x, y) and the point of this sequence of points Li (x1j, generalized distance D1 ² Mahalanobis between Y1j) Can be obtained by the following equation. D1 ² = ｛[(x-μ11) / σ11] ² + [(y-μ
12) / σ12] ² −2ρ1 [(x−μ11) / σ11] ×
[(Y-μ12) / σ11]｝ / (1-ρ1) ² Similarly, the point (x, y) of the previous point sequence and the current point sequence R
Mahalanobis' generalized distance D to point (x2j, y2j) of i
2 ² can be calculated by the following equation. D2 ² = ｛[(x-μ21) / σ21] ² + [(y-μ
22) / σ22] ² -2ρ2 [(x-μ21) / σ21] ×
[(Y-μ22) / σ22]｝ / (1−ρ2) ² As a result of calculating each general distance based on the above equations, D1
^2> if the D2 ² is satisfied, the last point (x,
y) is close to the current point sequence Ri on the right side of the road. If D1 ² > D2 ² does not hold,
Conversely, the previous point (x, y) is close to the current point sequence Li on the left side of the road.

As described above, even if the road candidate area obtained as a result of the image division processing becomes an area having an unclear contour due to noise or the like, the road candidate area can be determined based on the change in the histogram value of the window according to the present method. Can be reliably obtained. This is because the present method searches for the end point based on the region, and according to the present method, the left and right ends of the region can be clearly distinguished.

The low-brightness area obtained by the “extraction means for shadows and high-brightness areas on the traveling course paying attention to the difference in brightness” is located outside the road when the traveling course is entirely dark. In some cases. In such a case, the low luminance area outside the road and the road area may be merged, and the shape of the contour of the road candidate area becomes complicated. However, with this method,
By smoothing the obtained point sequence data at the road edge,
A region with a complicated contour is globally evaluated, and the low-luminance region as described above is ignored, so that information on a road edge that does not hinder the driving of the vehicle can be obtained.

According to the “traveling course extraction method using repetitive threshold processing using template images”, when an autonomous traveling vehicle is extremely close to the left or right of a road with a sharp curvature, the template is moved out of the road. Sometimes it comes out and regards it as a road. In this case, the left and right recognition of the road edge is erroneously performed. However, according to the above-described method, even if the data at the right and left ends of the road is erroneously recognized, this recognition is immediately corrected, and highly reliable road edge information can always be obtained.

Next, a description will be given of a "method of internally expressing traveling courses of various shapes", that is, a method of structuring and expressing the obtained various points at the road edge.

The present method is based on a method of extracting a running course obtained by “a running course extracting means by a repetitive threshold processing using a template image” and “shading and high brightness from the running course focusing on the difference in brightness”. Based on the image obtained by the "part extraction means" and the feature list obtained by the "multiple area merging means", the structure of the boundary point of the area is created while detecting the label displacement part of the image, and By assigning the link of, and giving the attribute of the boundary, the traveling course is expressed inside the computer, and an effective point sequence group for traveling is obtained. Using the feature list representing the description of the areas created by the "means for merging a plurality of areas", the end points of the areas can be obtained at high speed,
The roadside end, the branch road, and the junction are represented by a list structured inside the computer.

FIG. 45 is a flowchart showing an outline of the road structuring process.

First, a list in the y direction of the area boundary point structure is created at the time of initial setting of each hardware such as a color camera input device (step 4501). Next, a change in the x direction of the label value attached to each area is detected to create a list in the x direction, and a link is made in the x direction at each boundary point (step 4502). The attribute is given to the left end and the right end of the area in consideration of the connection label of the feature amount list of each area, and the left and right ends of each area are classified (step 4503). Next, while giving an attribute such as a hole to be described later to each boundary point, each boundary point in the front-rear direction (y direction) of the screen is associated, and a link is made in the y direction of each boundary point (step 4504). In addition, there are cases where the road area branches or merges. In each case, the continuity of the boundary of each area is determined, and the attribute assigned to each boundary point is changed (step 4505). Thereafter, the start position of the point sequence at the boundary point of each area is selected, and an effective point sequence is detected (step 4506).

Each of the above processes will be described in detail below. The processing in step 4501 will be described with reference to FIG. FIG. 1A is a diagram showing the principle of projective transformation of a road surface (assumed as a horizontal plane) in real space onto an image. Road surface 4601 is divided into equal intervals (l),
The road area and each lane marking on the road are projected onto the image 4602 toward a distant point to obtain a color camera viewport. FIG. 2B is a diagram showing details of the image obtained in this manner. On the image, the boundary line 4603 and the division line 4604 of the road area are projectively transformed.
Although the dividing lines in the real space were at equal intervals l,
In the image, the distance between the road areas becomes shorter as the distance from the road area increases. Y where each section line 4604 is located
The coordinates are obtained, and a region boundary point structure 4605 in the y direction shown in FIG.

Each frame of the area boundary point structure 4605 corresponds to each boundary point located at the intersection of the leftmost boundary line and the division line 4604 among the boundary lines 4603 of the road area. In each frame, a mutual connection relationship is represented by a pointer indicated by an illustrated arrow. In each of these frames, the characteristics of each boundary point are stored in a list 46 shown in FIG.
06. First, each value of the obtained y coordinate is described in a list 4606, and a list in the y direction is created. In this list 4606, in addition to the above-mentioned y-coordinate, the following characteristics of each boundary point are described.

That is, the x coordinate, the attribute representing the point sequence starting point iD, the attribute representing the distance between adjacent points in the real space, and the right adjacent representing the connection between the boundary point of interest and the boundary point on the right. The pointer, the pointer to the left indicating the connection with the boundary point on the left, the pointer to the front indicating the connection with the boundary point in front of the screen in the y direction, and the connection to the next boundary point behind the screen. Describes the next pointer to be represented.

Next, the processing in step 4502 will be described with reference to FIG. An image 4701 shown in FIG. 17A is an image obtained by performing a labeling process on a captured image. By this labeling process, a label value is assigned to each of the regions constituting the road region. Among the intersections between the partition lines and the boundaries of the regions, the intersections marked with a triangle are the label displacement points where the label values change. First, the image is scanned in the horizontal direction, that is, in the x direction at each y coordinate position of the list obtained in the processing of step 4501. In this scanning, when moving from a background area having a label value of 0 to a road area having a label value other than 0, or when moving from a road area having a label value other than 0 to a background area having a label value of 0, Create a new region boundary point structure.

This area boundary point structure is shown in FIG.
Is shown in As shown in the drawing, a frame is newly provided in the x direction corresponding to each boundary point located at the intersection of the boundary of each area and the dividing line. Each new frame is connected to each frame of the y-direction region boundary point structure created in the above-described steps. This connection relationship is represented by a pointer represented by an arrow shown, and the frames are linked in the horizontal direction by the pointer.

Next, regarding the processing of step 4503,
This will be described with reference to FIG. It is determined whether a set of points in the horizontal direction of the region boundary point structure created in the process of step 4502 exists in the same region. The determination as to whether or not the areas are the same is made based on the feature list obtained in the above-described “method of merging a plurality of areas”.
When adjacent points exist in the same area, the boundary point located on the left side is a left edge, and the boundary point located on the right side is a right edge, and this attribute is described in each list. With this attribute, the left and right ends of the area can be distinguished. Further, the distance in the real space from the left edge to the right edge is calculated, and similarly described in each list. In this distance calculation, it is necessary to execute a reverse projection conversion process, which is a process reverse to the above-described projection conversion, to obtain an actual road region from the image.

With respect to the image shown in FIG.
The edge identification method will be specifically described. Road area 480
1 is represented by merging three areas A, B, and C. Here, the connection labels of the above-described feature amount list are shown as in FIG. That is, the area C is described in the connection label section of the area A, nothing is described in the connection label section of the area B, and the area A is described in the connection label section of the area C. Therefore, it is determined that the area A and the area C are the same area to be merged, and that the area B is different from the area formed by the area A and the area B.

Therefore, when each boundary point is horizontally image-scanned along the dividing line 4802, the boundary points a, b, c, and d marked with ○ are obtained as label displacement portions. Therefore, the boundary point a and the boundary point b exist as points on the same area, and the boundary point a is provided with an attribute of a left edge and the boundary point b is provided with an attribute of a right edge, and is described in a list of area boundary point structures. You. Since the boundary point c and the boundary point d have the same label value, they exist in the same area. The boundary point c has the left edge and the boundary point d has the right edge. Is described in the list.

Next, regarding the processing of step 4504,
This will be described with reference to FIGS. 49 and 50. In the image shown in FIG. 49 (a), each boundary point located at the intersection of the left boundary line and each partition line is located at ai, ai + 1, and the right boundary line is located at the intersection of each partition line. Let each boundary point be bi,
bi + 1. Here, it is determined whether or not the i-th and (i + 1) -th boundary points in the y direction of the region boundary point structure are present in the same region. That is, it is determined whether the boundary points ai and ai + 1 are in the same area, and whether the boundary points bi and bi + 1 are in the same area. In the case shown in the figure, since each boundary point is in the same region, in the region boundary point structure shown in FIG. 3B, the boundary points located before and after the screen are linked by a pointer indicated by a thick line. .

If there is an internal point between the left edge and the right edge, the boundary of the area is traced as shown in FIG. FIG. 11A shows a white line 50 which is a lane dividing line in the center of a road area 5001 to be processed.
02 is an image on which the image is drawn. The label value of this white line 5002 is 0. Here, when the image is scanned in the horizontal direction along each section line, the internal points E1i, E1i + 1, E2i, and E2i + 1 shown in FIG. Therefore, the boundaries of the area are tracked to explore the relationship between each interior point. When this boundary is traced along the boundary of the white line 5002 from the internal point E1i, the internal point E1i + 1 is reached. When the boundary is traced from the internal point E2i, the boundary reaches the internal point E2i + 1. When the adjacent internal points E1i and E2i are continuous with the next internal points E1i + 1 and E2i + 1 on the partition line located above, the internal point E1i located on the left side is set to the internal point left edge. , The internal point E2i located on the right side is given the attribute of the internal point right edge.

When the internal points reach adjacent internal points without continuation of the internal points on the upper dividing line by tracking the boundary, an attribute of a hole is given to each adjacent internal point. That is, when the boundary is traced from the internal point E1i + 1, it reaches the adjacent internal point E2i + 1. In this case, each internal point E1i + 1, E2i + 1
Is given an attribute called a hole. The above processing is performed for each y in the list.
The process is performed until the y coordinate reaches the horizon position 5003 along the coordinates. At this time, the distance L in the real space from the left edge of the internal point to the right edge of the internal point is calculated. This calculation is obtained by subtracting the x coordinate value of the left edge point in the real space from the x coordinate value of the right edge point in the real space.

Next, regarding the processing in step 4505,
This will be described with reference to FIG.

In the method of assigning attributes by the processing of each step described above, a correct attribute is not assigned when a road branches in a Y-shape or when roads merge. That is, there is a case where the front-back direction association of each boundary point is not performed correctly. Therefore, in such a case, in this step,
Track boundaries and correct boundary evaluation.

For example, it is assumed that the image shown in FIG. 51 (a) has been obtained by the processing of each step up to this point.
The road region 5101 in this image is branched in a Y-shape, and is divided into a main road 5102 and a branch road 5103. Each division line (i, i +
, I + 2,...) Are image-scanned, and the image is scanned from the left side to the right side of the screen at each division line. As a result of this scanning, the boundary point located at the intersection of the leftmost boundary line and each division line is denoted by a, and the boundary point located at the intersection of the boundary line and the division line located one to the right of the leftmost boundary line is located. The boundary point is denoted by b, and the boundary point located at the intersection of the boundary line and the division line located two right to the left end boundary line is denoted by c. As a result, the region boundary point structure shown in FIG.

As can be understood from the figure, the association of each boundary point by this area boundary point structure does not match the actual road boundary, and each boundary point is not given a correct attribute. Therefore, the boundary of the area is tracked from the interior point, and if the next point is a boundary point existing at the left or right edge of the area, it is determined that the interior point and the next point are connected, The link of each point is replaced. In the case of the figure, the boundary of the area is traced from the internal point ci + 4.
When tracking, the next point is ai + 5, and since this point is the end of the left area, the link is replaced. That is, ai + 5
The attribute given to each subsequent boundary point is the same c attribute as the internal point ci + 4. By this link replacement processing, an area boundary point structure shown in FIG. This structure conforms to the actual road boundaries.

FIGS. (D) and (e) are diagrams showing the state of the area boundary before and after the link replacement processing by vector expression. That is, FIG. 11D shows a vector state before the link replacement, and the vector represents the connection relationship between the boundaries based on the area boundary point structure shown in FIG. The vector with the attribute a and the vector with the attribute c do not correspond to the actual road boundary. FIG. 11E shows a vector state after the link replacement processing is performed, and the vector represents the connection relationship of each boundary line based on the modified area boundary point structure shown in FIG. . The terminal part of the vector to which the attribute a is attached is modified as described above, and is replaced with the attribute c. For this reason, the boundary of the road area represented by each of the vectors a, b, and c has a shape conforming to a real thing.

FIGS. (F) and (g) are diagrams showing the state of the region boundary when the roads merge by using vectors in the same manner as described above. FIG. 11F illustrates the area boundary before the link replacement by a vector.
The vector with the attribute d blocks the entrance of the merging path formed by the vectors with the attributes e and f. FIG. 9G illustrates the area boundaries after the link replacement processing similar to the above by using vectors.
The start portion of the vector to which the attribute d is attached is corrected to the attribute f by the link replacement process, and the area boundary matches the actual merging path.

Next, regarding the processing of step 4506,
This will be described with reference to FIG. For example, assume a road image shown in FIG. This road image includes a road area 5201, and a white line 52
There are road shoulders 5203 separated by 02. A boundary point is provided at the intersection of the boundary line and the division line of each area of the road image, and a point sequence at the road end and the road shoulder end shown in FIG. Note that points that fall on the edge of the image are excluded from the point sequence. The links of the respective region boundary point structures in the y direction obtained by the operations of the above steps are traced from a position corresponding to the lower part of the image to the upper part. When the number of links of the area boundary point structure is equal to or more than a predetermined number, an attribute called a start point of a point sequence is attached to a portion corresponding to the lower part of the image of the link of the area boundary point structure. The starting point of the illustrated point sequence is indicated by a triangle.

[0211] Among the structures having the attribute of the starting point, a set of structures whose distance in the real space from the left edge to the right edge of the area boundary is equal to or larger than the width of the traveling vehicle is defined as a road edge. In the case of FIG.
And a set of structures corresponding to the point sequence 5205 is the road edge. Also, there is a set of structures next to the road edge, the distance between this structure group and the road edge is smaller than the width of the structure group representing the road edge, and the distance between the structure group and the other road edge. Is larger than the vehicle width, and the actual distance between the structures is continuous at about 20 cm, the structures form a white line. Then, a portion sandwiched between the structure group and one road end is regarded as a road shoulder divided by a white line.

[0212] In the point sequence shown in FIG.
A white line is formed by a set of structures corresponding to 6 and the point sequence 5207, and a portion between the white line and the road edge 5204 is regarded as a road shoulder.

Incidentally, the above-mentioned "means for obtaining a travelable range" is for recognizing a traveling course mainly on an area, and it is premised that a road area obtained as a result of image processing is one area. According to this means, it is possible to identify a road end of a monotone road or a branch road. However, it is difficult to distinguish and handle a region divided by a white line, particularly, a road and a road shoulder. This is because the means scans the window from the inside of the area to search for the area boundary, so that the road edge and the shoulder edge cannot be distinguished at the same time.

However, as described above, the label image is horizontally scanned from the lower part to the upper part of the image, the boundary of the local area is traced, and the displacement of the label value of each area is obtained, thereby obtaining the boundary of the area. You can know. With this,
By updating the relationship between the areas in the structure and structuring the boundary points of the areas, the road area is structured and represented inside the computer. Therefore, according to the “internal representation method of traveling courses of various shapes”, it is possible to obtain the boundary end coordinates of a plurality of regions such as a road shoulder and a traveling lane, to easily detect the region boundaries, and The target region is not limited to a single region as in “means for obtaining range”. Further, the road shoulder and the like separated by the white line can be identified.

Further, the memory capacity is smaller than that of the conventional area boundary method, and the travelable range of the traveling course sufficient for the autonomous vehicle to automatically travel is determined by successive boundary search or polygon approximation. Can be obtained without performing processing such as performing evaluation. Furthermore, even when the traveling course is not a monotonous road and the road branches or merges, the shapes of these branching roads and merging passages can be uniformly expressed, and the boundary of the traveling course can be accurately determined. Can be identified.

As described above, according to the “internal representation method of traveling courses of various shapes”, the connection relation between boundary points of each area is represented in a structured structure by the attributes described in the list. From this structure, the boundaries of the region of interest are identified. For this reason, since the number of point sequence groups determined as in the related art is large, the process of approximating the boundary of the target area with a polygon and eliminating unnecessary points is eliminated. Further, it is not necessary to sequentially track all the boundary pixels of each region one by one as in the related art, and the target region is identified by processing only predetermined boundary pixels. Therefore, the processing time is reduced, and an area identification method suitable for the automatic traveling of the autonomous traveling vehicle is provided.

[0219]

As described above, according to the present invention,
Since the process of determining the connection between the respective regions is performed based on the pixels of each region that has been divided and labeled with the image, the operation of changing the label value, which takes a long processing time for merging the regions, is unnecessary, and the time is short. Allows the merging process of a plurality of areas to be performed. Therefore, high-speed processing required for autonomous traveling of the automobile can be performed, and processing costs can be reduced.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a schematic configuration of an entire color image processing apparatus according to an embodiment of the present invention.

FIG. 2 is a flowchart illustrating a flow of a schematic process according to the embodiment.

[FIG. 3] "IS of color image using ROM table"
It is a block diagram which shows the algorithm of the ISH conversion process in "H conversion process."

FIG. 4 is a diagram for explaining a processing example of the ISH conversion processing in comparison with a conventional processing example;

FIG. 5 is a diagram for explaining a processing example of the ISH conversion processing in comparison with a conventional processing example;

FIG. 6 is a diagram for explaining a processing example of the ISH conversion processing in comparison with a conventional processing example.

FIG. 7 is a diagram for explaining a processing example of the ISH conversion processing in comparison with a conventional processing example;

FIG. 8 is a flowchart showing an outline of preprocessing of a color image in “threshold setting means based on the shape of a feature amount histogram in repeated threshold processing”.

FIG. 9 is a diagram for explaining the states of peaks and valleys in this preprocessing.

FIG. 10 is a diagram for explaining a transition of a table value in the preprocessing.

FIG. 11 is a flowchart showing an outline of a travel path recognition process.

FIG. 12 is a flowchart showing details of a process of extracting a traveling route in “means for extracting a traveling course by a repeated threshold process using a template image”.

FIG. 13 is a diagram for explaining each process in the road candidate region extraction processing.

FIG. 14 is a diagram for describing an outline of processing for various input images in “extraction means for extracting a shadow or a high-luminance portion from a traveling course paying attention to a difference in brightness”.

FIG. 15 is a graph showing an example of a brightness histogram for explaining a shadow and a high-luminance portion in this means.

FIG. 16 is a configuration diagram of a labeling board of the “labeling processing device”.

FIG. 17 is a general flowchart of a labeling process.

FIG. 18 is a diagram for explaining a multi-level input labeling method.

FIG. 19 is a diagram for explaining a labeling method using a run.

FIG. 20 is a diagram illustrating windows used in image scanning of the labeling process.

FIG. 21 is a configuration diagram of a labeling processor KLP.

FIG. 22 is a flowchart illustrating window processing performed in image scanning of the labeling processing.

FIG. 23 is a flowchart illustrating window processing performed in image scanning of the labeling processing.

FIG. 24 is a flowchart illustrating window processing performed in image scanning of the labeling processing.

FIG. 25 is a flowchart showing window processing performed in image scanning of the labeling processing.

FIG. 26 is a flowchart illustrating window processing performed in image scanning of the labeling processing.

FIG. 27 is a flowchart illustrating window processing performed in image scanning of the labeling processing.

FIG. 28 is a configuration diagram of a labeling memory KLM.

FIG. 29 is a diagram for explaining a process of matching label values finally given.

FIG. 30 is a configuration diagram of a feature extraction processor KLC.

FIG. 31 is a diagram illustrating a one-scan labeling method.

FIG. 32 is a diagram showing an inverted L-shaped mask used for exploring a label area in “means for merging a plurality of areas”.

FIG. 33 is a diagram showing an example of a divided area used for explaining an area boundary search using the inverted L-shaped mask shown in FIG. 32;

FIG. 34 is a diagram showing an inter-label boundary length matrix in which the boundary length between label regions and the perimeter of the region obtained by the inverse L-shaped mask scanning method are stored.

FIG. 35 is a diagram illustrating a feature amount list in which connection relationships between regions and feature amounts of each label region are described.

FIG. 36 is an example of a label area having each feature described in the feature list shown in FIG. 35;

FIG. 37 shows a reference pixel position No. assigned near the pixel image of interest in the boundary search method. FIG.

FIG. 38 is a diagram showing a pixel reference table used in the boundary search method.

FIG. 39 is a diagram showing an example of a pixel area used for describing a method of using the pixel reference table shown in FIG. 38.

FIG. 40 is a flowchart showing a flow of processing of a traveling course recognition system in “means for obtaining a travelable range”.

FIG. 41 is a diagram showing movement of a window in a captured image used in the description of this means.

FIG. 42 is a diagram illustrating values of a window and a histogram in a target area.

FIG. 43 is a diagram for describing a reversal of road edge identification that occurs when a traveling vehicle approaches a road edge too much due to a sharp curve.

FIG. 44 is a diagram for explaining a method of correcting the relationship between the point sequence groups obtained to prevent the inversion of the recognition of the road edge shown in FIG. 43 using Mahalanobis' generalized distance.

FIG. 45 is a flowchart showing an outline of a road area structuring process in the “internal representation method of a traveling course having various shapes”.

FIG. 46 is a diagram for describing a y-direction list and an area boundary point structure.

FIG. 47 is a diagram for explaining a label displacement portion and an area boundary point structure.

FIG. 48 is a diagram illustrating attributes of a region boundary.

FIG. 49 is a diagram for explaining link processing in the front-back direction of an area boundary point.

FIG. 50 is a diagram for describing attributes of an internal point edge and a hole.

FIG. 51 is a diagram illustrating a process of replacing an attribute once assigned.

FIG. 52 is a diagram for describing detection of a road edge and a road shoulder edge. 101: color camera, 102: ISH converter, 1
03: color processing unit, 104: labeling hardware,
105 CPU processing unit.

Continued on the front page (72) Inventor Akira Nagao 1-4-1, Chuo, Wako-shi, Saitama Pref. Inside Honda R & D Co., Ltd. (72) Inventor Hayato Kikuchi 1-4-1, Chuo, Wako-shi, Saitama Co., Ltd. Inside Honda Technical Research Institute (72) Inventor Taku Sugawara 1-4-1, Chuo, Wako-shi, Saitama

Claims (2)

[Claims] 1. A mask is scanned with respect to pixels in each region divided and labeled with an image, and the same is determined based on a connection relationship between a label value of a pixel of interest appearing in the mask and a label value of a pixel adjacent to the pixel of interest. A common boundary length between the peripheral length of the label region and the region adjacent to the same label region is obtained, and a connection degree between the adjacent regions is obtained based on a ratio between the peripheral length and the common boundary length. Is described in a memory to determine a connection relationship between the areas, and a method of merging a plurality of areas in a color image. 2. A pixel reference table in which a number indicating a position is assigned to each pixel located in the vicinity of a pixel of interest and the position numbers are arranged in a predetermined order in a column direction and a row direction, and an image is divided and labeled. By paying attention to one pixel at the boundary of the attached area and examining the label value of the pixel in the vicinity of the pixel, the boundary of the area near the pixel is locally searched. The position number of the next pixel to be searched located at the boundary of is determined based on the searched pixel, and the next pixel to be searched next is determined according to the position number column of the pixel reference table specified by the position number. By searching for a pixel in the vicinity of, a boundary of a region near the pixel to be searched next is locally searched, and pixels located in the vicinity of each pixel at the boundary of the region are sequentially searched for by the pixel reference table in the following order. A local search is performed in accordance with the designated position number sequence, a perimeter of the region and a common boundary length with the region adjacent to the region are obtained, and a distance between the adjacent regions is determined based on a ratio of the perimeter and the common boundary length. A method of merging a plurality of areas in a color image, wherein a degree of connection is obtained, and the degree of connection is described in a memory to determine a connection relationship between the areas.