JPH01281577A - Image processor - Google Patents

Abstract

PURPOSE:To rapidly execute image data processing at real time by means of a miniaturized hardware by serially connecting plural digital differential analytical circuits with the same constitution, extracting the feature point of an original image and executing a rotating motion recurrence formula. CONSTITUTION:A picture element in each point to be processed on an original image inputted by a camera 11 is inputted through a signal input part 12, its edge is detected by an edge detecting part 13 and the edged data are sent to a preprocessing part 17. The preprocessing part 17 executes prescribed preprocessing and successively sends the processed result to an initial value operating part 40. The operating part 40 previously executes the operation of an initial value for executing the operation of the rotating motion recurrence formula for finding out a Hough curve by means of the digital differential analytical(DDA) circuits 18. The DDA operation part 18 continues the operation until the whole processing of picture elements other than a window or excluded picture elements to be less than a threshold by the preprocessing part 17. After ending the processing, the filtering processing 19 and sorting processing 20 of the intersecting point of the Hough curves are executed to find out an approximate straight line of a curve connecting the points to be processed on the original image.

Description

DETAILED DESCRIPTION OF THE INVENTION [Industrial Application Field] The present invention relates to an image processing device, which is used for controlling unmanned vehicles, real-time recognition of the motion of an observed object, and the like.

[Conventional technology]

For example, when controlling unmanned robots or autonomous vehicles, it is necessary to use a camera to capture images of identification lines displayed in advance on the road ahead, or the center line or shoulder line of the road, and process the images in real time.

Figure 20 is for explaining road recognition using images. Figure 20 (a) is an image captured by a camera, Figure 20 (b) is an image captured by a camera.
is the luminance (or its rate of change, etc.) in figure (a)
3 is a diagram showing pixels with high values as black dots. FIG.

As shown in Figure (a), the camera image 1 shows a road 3 extending toward infinity from the horizon 2. Road shoulder lines 4 are drawn on both sides of the road 3, and a center line 5 is drawn in the center. is depicted. At Uni, the shoulder line 4 and center line 5 of road 3 have higher brightness than other parts.
Therefore, in the same figure (b), dots 4' and 5 are placed in these parts.
' will appear consecutively. In order to recognize the traveling direction and curved shape of the road 3 in such a camera image 1, it is sufficient to recognize the approximate straight lines L, L, L, etc. of the curve connecting the dots 4' in FIG. .

Traditionally, as a method for finding such an approximate straight line L,
A technique called Hough transform is known (for example, US Pat. No. 3,069,654). This is the second
This will be explained with reference to FIGS. 1 to 23. As shown in FIG. 21(a), when a processing target point P (x ry ) exists in the original image indicated by the x-y coordinate system, the line p (such as IC1.1lb) passing through this point P is infinite. I can only draw.

And this straight line 1, g. Regarding the straight line that is orthogonal to b and passes through the origin 0 (0.0), the straight line j!
, 1 , ... can be drawn one by one.

b12, regarding the straight line passing through the origin 0 (0,0), the straight line lCf1. I, etc.) to ρ(ρ, .

ρ, etc.), and the angle between it and the X axis is θ (θ, θ.

a ), then the above ρ of the straight line passing through this origin.

θ is expressed as a sine curve, ie, a Hough curve, as shown in FIG.

Here, the origin 0 (0,0) and the processing target point P(x
, y) is the distance ρ of this processing target point 1)
The above ρ(ρ3
．． ρ6. ...), which is 2 2.1/2 ρwax"(x"p), and when θ-0, it becomes ρo-x.

Next, three points P lined up in a straight line as shown in Figure 22(a)
- When the Hough transformation shown in Figure 21 is applied to P3, the sine curve (Hough curve) for point P1 becomes as shown in the dotted line in Figure 22 (b), and the sine curve for point P2 becomes as shown in Figure 22 (b). The sine curve for point P3 becomes like the dashed-dotted line in FIG. Here, the peaks (ρ, θ), (ρ, θ) and (ρ
, θ) are the origin 0 (0,0) and points P, P2 . 3 corresponding to the distance pH~ρ between P3 and the angle θ~θ3 formed with the X axis.

In Fig. 22(b), if we pay attention to the point where the three Hough curves (sine curves) intersect, we can see that the coordinates here are (ρ, θ), which corresponds to the direct t in Fig. 22(a).
ρ of the straight line passing through the origin 0 (0,0) perpendicular to lL,
It is equal to θ. Therefore, if we find the intersection point of such a sine curve, we can find the approximate straight line of the curve between the dots (black dots) drawn in the x-y orthogonal coordinate system of the original image (however, in Fig. 22, this curve and the approximate straight line are Match)
can be found.

To explain this with reference to Fig. 23, first, in Fig. 23 (a), there are many dots (processing target points) to be Hough-transformed on the x-y coordinate plane (original image plane), and these are lined up on a curve. shall be.

Here, in the same figure (a), three approximate straight lines, L and L3, can be drawn on the curve connecting the dots. Therefore, if all of these dots are converted into sine curves (Hough transformation) as shown in Figure 21, the intersection points of the sine curves as shown in Figure 21(b) will be obtained centered on three locations. become. The coordinates of this intersection point are
(ρ θ ), (ρt2'tl
' tl θ ) and (ρ θ ), so this t2
When t3' 13 is expressed in the coordinate system of ρ, θ, H, where H is the frequency of appearance of the intersection point, the result is as shown in FIG. 3(b).

Therefore, the approximate straight lines L1 to L3 of the curve corresponding to the shoulder line 4 of the road 3 as shown in FIG.
This can be determined by the values of ρ and θ at the peak of H (frequency of appearance of crossover points) in FIG.

[Problem to be solved by the invention]

However, it has not been easy to apply the Hough transformation method as described above to high-speed and real-time image processing. This is because the coordinate values (x,
When trying to find the distance from the origin to the straight line g in order to find the p Hough curve (sine curve) based on y), ρ-x
eslnθ+y·eolJθ (1) p must be executed. For example, if θ is divided into 512, trigonometric function calculations must be performed 1024 times, multiplications 1024 times, and additions 512 times. If the original image to be calculated is composed of, for example, 512×512 pixels, the total number of calculations will be extremely large, and processing with a normal processor will significantly increase the processing time.

Of course, the sin θ required in the above equation (1)
It is also possible to shorten the calculation time by storing the values of F.R.
"Hough conversion hardware using OM" 1986 IEICE 70th Anniversary General Conference, No.
.

1587). However, this method requires a large-capacity ROM, and the access time cannot be ignored. Furthermore, even if the trigonometric function data is stored in ROM, the number of multiplications in the calculation of equation (1) remains the same as before, and considering that the multiplication time is considerably longer than the addition time, there is no fundamental solution. It cannot be.

On the other hand, if we examine the above-mentioned document ``Ha u gh conversion hardware using FROM'' from a different perspective, here we can improve the signal processing as a whole by parallelizing the calculation section that performs the calculation of equation (1) above. We are trying to speed it up. However, in this case, a memory table (ROM) for determining sin θ and COS θ is required as many as the number of arithmetic units connected in parallel, resulting in an extremely large-scale hardware system.

For this reason, it has been almost impossible to control a vehicle traveling at high speed in real time based on image data, or to recognize a wave observation object moving at high speed in real time based on image data. Furthermore, when attempting to increase the speed by parallelizing the arithmetic units, it was inevitable that the hardware would become larger.

SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide an image processing apparatus that is not only capable of processing image data at high speed in real time, but also capable of reducing the size of the hardware.

[Means to solve the problem]

A first image processing device according to the present invention is an image processing device that separates and extracts feature points of an original image from a plurality of processing target points on the original image captured by an imaging means, and includes the following elements. Be prepared. In other words, D D A (
Dlgltal Di “rerentlal^naly
sis (Digital Differential Analysis) DDA calculation means configured by connecting a plurality of calculation elements in series, storage means for storing calculation results of the DDA calculation means in correspondence with the DDA calculation elements, and storage contents of the storage means. Basically, it is characterized by comprising means for extracting feature points of the original image.

Further, a second image processing device according to the present invention is an image processing device that derives an approximate straight line of a curve connecting a plurality of processing target points on an original image captured by an imaging means, and includes the following elements. . In other words, let the coordinates of a point on the approximate circle drawn in the α-β orthogonal coordinate system be (α, β), and let the rotation angle of the next point II on the circle to locus 1 (α, β) be ε. D.I.
When DI (where i is a positive integer), αI + 1 - f (α, βi, ε) α I β - f (α, β, ε) Country β II A DDA calculation means that sequentially performs the calculation in the Vibrine method for each time, a storage means that stores at least the value of β1 among the respective results sequentially calculated by the DDA calculation means, and a The method is characterized by comprising an approximate straight line deriving means for deriving an approximate straight line of the curve from the intersection of the Hough curves for each of the plurality of processing target points obtained.

[Effect]

According to the configuration of the present invention, a calculation unit can be easily formed by simply connecting DDA calculation circuits (calculation elements) of the same configuration in series, and there is no need to refer to a memory table or the like during calculation. It becomes significantly simpler and smaller.

Furthermore, by configuring the arithmetic circuit to execute the rotational motion recurrence formula in a pipeline manner, it is possible to speed up the calculation.

[Example] Hereinafter, based on FIGS. 1 to 19 of the attached drawings,
The present invention will be described in detail. In addition, in the description of the drawings, the same elements are given the same reference numerals, and redundant description will be omitted.

FIG. 1 is a block diagram showing the overall configuration of an image processing apparatus according to an embodiment. In the figure, a camera 11 captures an original image by capturing an object to be processed (for example, a road, a high-speed moving object, etc.), and this image signal is digitized by a signal input section 12 and sent to an edge detection section 13. . The edge detection unit 13 extracts the edges of the image signal and generates edge data with shading, for example, 512
The signal is sent to the multilevel memory 4 as X512 pixel signals (edged pixel signals). The multi-level memory 4 stores the aging data for each pixel, and sends the aging data to the D/A converter 15 each time one screen is scanned, and this is given to the CRT display 16 as an analog signal. Therefore, this edged data is displayed on the CRT display 16.

On the other hand, the aged pixel signal output from the edge detection section 13 is given to the preprocessing section 17, and the edged pixel signal is subjected to preprocessing as will be described in detail later. The DDA calculation unit 18 is provided with n DDA calculation circuits 18 to 18o-1, which are connected in series to each other.The output side of the DDA calculation unit 18 is The neighborhood filter 9 and the noting unit 20 are connected, thereby performing neighborhood filtering processing and sorting processing (described in detail later).The above circuit elements are connected to the C'PU 22 via the VME bus 21. The signal processing operation is controlled and the processing timing is synchronized.
The preprocessing section 17, the DDA calculation section 18, and the neighborhood filter 9 are connected to each other via a VME bus 23, and the transfer of the DDA calculation results and the transfer of gray value data are synchronously controlled.

Next, the detailed configuration of the main parts of the image processing apparatus shown in FIG. 1 will be explained.

FIG. 2 is a configuration diagram thereof, which corresponds to the preprocessing section 17, initial value calculation section 40, DDA calculation section 18, and neighborhood filter 9 in FIG. As shown in the figure, the preprocessing section 17
This is realized by an FO (First-In First-Out) board 17'. The FIFO 17' stores the coordinate value (X) of the point to be processed on the X-Y plane.

Y) is input as an address signal, and edged gray value data D is input as a data signal.

Then, as described later, this PIF017' performs coordinate conversion from X-Y coordinates to Output to.

The initial value calculation section shown in FIG.
/F41 is the coordinate value (
x, y) are temporally stored, and the F/F 42 temporally stores the grayscale value data D of the processing target point P. Then, the initial value calculation unit ■ path 43 calculates αp,
The coordinates of the initial value (α.

β. ) to enable the calculation of rotational motion recurrence formulas.

The DDA arithmetic circuits 18o to 18 in each stage shown in FIG. /F
31 temporarily stores the address signals α0 to αn-1° β to β, respectively, and F/FOn-1 32 temporarily stores the gray value data DI, respectively.
/F 33 is RAM34 (RAMo~RAM
) Histogram data DN read from each of
O""" M(n-1) is stored temporally.

The DDA 37 (DDA-DDAn,) each performs a rotational motion recurrence formula, which will be described later, for each rotation angle, and inputs address signals α and β1 and outputs address signals α and β, respectively. +1+I Il
l The calculator ADD35 (ADDo-ADDn,) is P
The gradation value data D from the IFO 17' is added to the histogram data DMo to DH (n-1>) from the RAM 34, and the output is temporarily stored in the buffer 36 and then stored in the RAM-RAM. The On-1 timing controller 25 controls the timing of signal processing in these circuit elements, and outputs timing pulses φ 1 and φ 1 to φ .

S a e and is connected to a command/status interface (I/F) not shown.

Next, an overview of the overall operation of the image processing apparatus shown in FIGS. 1 and 2 will be explained with reference to FIG. 3.

FIG. 3 is a flowchart explaining this.

First, pixel signals for each point to be processed on the original image captured by the camera 11 are inputted via the signal input section 12 (Step 102), and edge detection is performed by the edge detection section 13 (Step 104). The data is manually input to the preprocessing section 17 (step 106). Above steps 102-1
The processing of 06 is repeated every time a pixel signal is input, and the result (edge data) is sequentially sent to the signal preprocessing unit 17.
is sent as digital data.

The preprocessing unit 17 performs predetermined (described later) preprocessing (step 10
8), and the processed data is sent to the initial value calculation unit 40.
(step 110). This preprocessing is also sequentially repeated each time edged data is provided.

Next, the calculation of the rotational motion recurrence formula for obtaining the Hough curve (sine curve) is executed as a DDA calculation (step 112), as will be explained later, but prior to this calculation, the initial value is executed in step 111.

Then, the calculation in the DDA calculation unit 18 is performed on the pixel signals of one screen (original image plane) to be processed by the preprocessing unit 17.
continues until outside the window or below the threshold (
When step 114) is completed, filtering processing (step 116) and sorting processing (118), which will be described later, are performed on the intersection of the Hough curves by the neighborhood filter 19 and the sorting unit 20, and as a final result, the processing target point on the original image is An approximate straight line for the connecting curves will be found.

Next, the edge detection method and edged data in the edge detection section 13 will be explained with reference to FIG.

The original image captured by the camera 11 is shown in Figure 4 (a).
If we take out the line indicated by reference numeral 8 in the figure using the X' coordinate, we can see that the luminance S is shown in analog form as shown in figure (b). That is, road 3
The brightness of the outer part of the road 3 and the road shoulder is low, but the brightness of the road shoulder line 4 is very high. Here, in Figure 4 (
In the embodiment, the brightness distribution as shown in b) is recognized as digital data of 256 gradations, but understanding the distribution of brightness itself is not sufficient to accurately recognize the shape of the road. .

Therefore, when the brightness S of the pixel signal obtained through the signal input section 12 is differentiated with respect to the coordinate X'(dS/dx') and understood as the rate of change in brightness, the edge becomes clear as shown in Figure 4(c). When this is expressed as an absolute value IdS/dx'l, it becomes as shown in the same figure (d), which is edge data (digital data) for clearly recognizing the edge of road shoulder line 4.
is obtained by the edge detection section 13. The edged data from the edge detection section 13 is subjected to predetermined preprocessing in the preprocessing section 17 in the next step.

FIG. 5 is a flowchart for explaining preprocessing in the preprocessing section 17 (FIFO 17').

First, the edge data obtained as shown in FIG. 4 is manually inputted from the edge detection section 13 to the preprocessing section 17 (step 122), and it is determined whether or not this data is within a preset window. (Step 124). This window is set, for example, as shown by reference numeral 9 in FIG. 4(a). Here, whether or not the data is edged inside window 9 is determined by the data (processing target point P).
The next step 126 can be determined based on the coordinate values (X, Y) on the X-Y coordinate plane of p, and the next step 126 is executed only for the edged data in the lower window 9.

In step 126, whether or not the edged data in the window 9 is equal to or higher than a predetermined threshold value (threshold level) is digitally determined using, for example, a look-up table (LUT) attached to the FIFO 17'. That is, as shown in Fig. 6, when the LUT is not provided, the input data and output data of the PIF017' have a one-to-one correspondence (as shown in Fig. 6(a)), but the threshold level can be set using the LUT. For example, if you set it to Ith,
When the input data is less than Ith, the output data becomes 0 (zero). Note that when the gradation value data included in this edged data is grasped in 256 gradations, the threshold level Ith can be set arbitrarily between 0 and 255. If this threshold value is shown in analog form, for example, Fig. 4(d)
is set as shown by the dotted line, so step 126
The processed data becomes data (digital data) mainly corresponding to the path line 4 and center line 5 in the original image. For this reason, the data to be subjected to signal processing (described later) is the main data (for example, data corresponding to the shoulder line and center line of the road), so it is no longer affected by noise components, and the overall processing speed is can be speeded up.

Next, in step 128, a coordinate transformation is performed.

That is, the X-Y coordinate shown in FIG. 4(a) is converted into the X-y coordinate. As a result, the coordinates (X, Y) of the processing target point in the original image are converted to coordinates (x, y) in the Xp-y coordinate system. The above processing completes the preprocessing for Hough transformation.

Note that the order of steps 124 to 128 in FIG. 5 may be different. For example, the coordinate transformation in step 128 may be performed first, but in consideration of the time required for data processing, it is considered most preferable to perform the coordinate transformation in the order shown in FIG.

Next, application of the Hough transform in this embodiment will be specifically explained with reference to FIGS. 7 and 8.

As already explained in Fig. 21, if a Hough curve (sine curve) is obtained for the point P (x, y) shown in Fig. 7(a), it will become as shown in Fig. 7(c). That's right. By the way, it is also easily understood from the theorem of trigonometric functions that the locus of such a sine curve can be replaced with the locus of circular motion as shown in FIG. In other words, Ho about point P (X, y p ) in figure (a)
Executing the Hough transformation to obtain the Hough curve shown in FIG. 12(c) is equivalent to obtaining the circumferential locus of circular motion as shown in FIG. 2(b). Here, the radius R of the circle in FIG.
aX '' yp and the circular motion starts from the point P(x, y), then its initial value θ is θd'-π/2-θsax, where tanθ-x/y (3 )la
It is X P p .

The inventor focused on this fact and applied the recurrence formula for circular motion when drawing the circle shown in FIG. 7(b),
We have found a method that can easily perform Hough transformation of point P (x, y) to p (c) in the same figure. Here, according to the recurrence formula for circular motion above, α−
Coordinates (α, , β ) of a point advanced by one rotation angle ε from a point expressed as coordinates (α , β1) in the β Cartesian coordinate system
When i is a positive integer, α, +l−f (α, β, ε) α 11 β −f (α, β, ε) ...(4) Country
It is determined as β li .

Several specific contents of this equation (4) have been known for a long time. For example, the rotation angle ε is expressed as ε-2 (rad
) (However, when m-0, 1, 2, ...), afil ""f -2β1 β -2-α1+βl ... (5) 1
+1 or afil -'I ''-2βI β -21α1+β, (1-2-”)...(6)1
There are some that give +1. In addition, α −α (1−2−”−1) −2βItri is found by the inventor to be more accurate and easier to calculate.
t β −2α +β (1-2-”-1) country
II ... (7) Or 2m-1 α -α (1-2) Country 1 +β (-2+ε /6) -1-3■ β -α (2+ε /6) Country I +β (1-2-2I -1) ■...(8) etc. may also be used.

Therefore, if we apply the recurrence formula of equation (7) above, the second
Prior to explaining the specific operation of the circuit shown in the figure, this calculation method will be specifically explained.

FIG. 8 is a flow chart thereof. First, data preprocessed by the FIFO 17' according to FIG. 5 is input (step 132), and initial values (α, β) for calculation of the rotational motion recurrence formula are determined. In Fig. 7, an arbitrary position (angle) θ' (-θo) (r
If we start the circular motion from a d), aO--x, sin θ'+yp cos θ'βo"Xpe
O8θ'+ypslnθ' (9). This (α, β0) is calculated in the initial value a section 40, and the DDA
It is given to the arithmetic unit 18 as an address signal. The initial value calculation unit 40 stores the values of sin θ' and cos θ' in advance in a ROM (not shown), and uses an adder and a multiplier (both not shown) while referring to these data. Then, the calculation of equation (9) can be performed. Here, the calculation of equation (9) requires more calculations and is more complicated than the calculation of the rotational motion recurrence equation, but it is necessary to calculate
Since only p is performed once for X, Y), the overall calculation time does not increase much, and the hardware does not become large. Note that this initial value calculation is not necessary when starting p circular motion from the processing target point P(x, y).

Next, β according to the above equation (9). After storing the value in the RAM, α1°βl is determined by equation (7). This can be obtained from the output of DDAo by substituting α0°β0 obtained from equation (9) into equation (7) (step 136
), DDA,.

The results (β, β, β,…) are sequentially stored in the RAM after each calculation in DDA, DDA, etc.
, RAM, RAM3, etc. (Step 138). On the other hand, between step 136 and step 138, gradation value data is accumulated.

That is, the histogram data DM1 read from the RAM 34 (RAM) and the grayscale value data D1 from the FIFO 17' are added, and this is stored again in the RAM (step 137).

Then, step 140) repeats this calculation for each rotation angle ε until it goes around the circle.
The Hough curves for the three processing and target points are β0°β , β , β3 . ... and θ, θ1. Not only can it be determined from the value of θ2゜... (rotation angle ε), but it can also be calculated from the weighting result using the gray value data (
Histogram data D , D , ... DM (.-
1)) is also required.

MOMl Below, when the processing shown in Fig. 8 is executed for all processing target points on the original image, multiple Hough curves weighted by gray value data will be obtained in the ρ-θ coordinate system, and these This results in a crossing point as shown in FIG. 22(b).

Next, the operation shown in the flowchart of FIG. 8 will be explained in more detail with reference to FIG.

First, data input at step 132 in FIG. 8 is performed by inputting a ready signal from the FIFO 17' in FIG.
After the coordinates (x, y) of the point P to be processed are stored in the F/F 41p, the gray value data DI at that point P is stored in the F/F 41p.
This is done by storing it in 2. Then, in synchronization with the timing pulse φ from the timing controller 25, (x, y and F/F 41S
p p, and the initial value calculation unit 4
0, the initial value p (α, β0) of the recurrence formula calculation is determined from the coordinate values (x, y) of the processing target point P.

The calculation of the recurrence formula in step 136 is performed by the initial rL calculation circuit 4.
The output from 3 is sent to FIFO as address signals α and β.
17' to the F/F 31, and manually inputs the gradation value data DI of the processing target point P from the F/F 42 to the F/F 32. Here, addresses and data input to the F/Fs 31 and 32 are performed in synchronization with timing pulses from the timing controller 25. Then, in synchronization with the rise or fall of the timing pulse φ, the address signals α and β of the F/F 31 are generated.

is manually input to the first DDAo (37).

In this DDAo, the process of step 136 in FIG. 8 is performed. That is, the calculation of the recurrence formula according to the above-mentioned equation (7) is executed, and the calculation results (address signals α 1 , β 2 ) are sent to the next DDA (not shown). put it here,
The above recurrence formula (7) basically does not include trigonometric function calculations or multiplications, and the memory table (R
Since there is no need to refer to OM), calculations can be performed easily and quickly. These have sufficient accuracy (few errors) to perform circular motion.

Note that this DDA is DDA −DDA and 0
1 n-1 is constructed in the same way, specifically, as shown in Figure 9, there are 4 additions)
'L2951-54 and three inverters 61-63 are included. Here, the symbol S in the figure.

S is each m bit shift, 2m+1 bits 2m+
1 shift. For the inputs α and β to the circuit of FIG. 9, the output is αtit'.

Address signal β stored in F/F 31. is RAM,
(34), and thereby the histogram data DH8 stored in RAMo is read out. That is, β for RAMo. One rotation angle θ as address
. Histogram data DMO regarding other processing points corresponding to
), and therefore the address signal β
By giving 0 from F/F 31 to RAM, RAM0 to F/F 3 synchronizes with timing pulse φb.
3, the histogram data DMo is sent.

Next, in synchronization with the timing pulse φ from the timing controller 25, ADDo (35
), but this AD
D is given the dark p-light value data D1 of the processing target point (x, y) which is the calculation target from the F/F 32. Therefore, in ADDo, the rotation angle θ and address β that have been stored in RAMo. Histogram data DMo corresponding to , and the rotation angle θ of the processing target point (x, y) that is being processed. and gray value data D1 corresponding to attopresβ are added (step 137 in FIG. 8). Then, this addition result (D
' -DMo+D, ) is temporarily held in the buffer 36:;O - and then sent to RAMo in synchronization with the timing pulse φd, and the RAMo address corresponding to θ is stored according to step 138 in FIG. β. It will be done to

The above one-cycle processing is performed by the DDA calculation circuit 18+
To explain (i=1, 2...n-1), the first
It will look like Figure 0.

First, from the coordinate values (x, y) of the point to be processed, p The obtained address signals (initial values) α and β are α. .

The address signal αl is the result of sequentially calculating β - α, β → α, β →...α, β1.

β and the shading value I of this processing target point (X, )')
pp data D1 is input from F/Fs 31 and 32 and held (step 202), and after address signals α and βI are sent from F/F 31 to DDA, address signals α1. The calculation of the recurrence formula based on β1 is executed in the DDA. The resulting address signals α and β are then sent to the F/F 31 provided before the 1+1 DDA in the next DDA calculation circuit 18 in synchronization with the end of one cycle of processing.
+

On the other hand, reading of the histogram data DMI using the address signal βI is executed in step 206. This step 206 corresponds to the address signal β1 in FIG.
is applied from the F/F 31 to the RAM, and the histogram data DMi at address β1 is stored in the F/F 33. Then, in step 208, the gray value data DI of the histogram data DM1 is added. This step 208 is executed in ADD of FIG. Thereafter, the histogram data 90 added in step 208
'-DM, +D, are separately written to address β0 of RAMs in step 21011.

Refer to FIG. 11 to explain the accumulation of histogram data in more detail.

FIG. 11 shows the concept of a histogram memory using the RAM 34 in FIG.
-RAM area, and these On-1 are On-1 at the rotation angle θ (-θ') to θ of recurrence formula calculation.
1 They correspond to each other. And each RAM area is +51
Addresses .beta.(-.rho.) from 2 to 0 to -512 are &L, and 16-bit histogram data (shade values) can be stored in each address.

Therefore, in D D A 1, α, , β corresponding to the rotation angle θ, are the address signal ++1 according to the above equation (7).
When calculated from ++l I11αl and βI, the address signal β is applied to the RAM and the histogram data D at the address β1 is read out. And M(
1) The histogram data (DH, +D,) is added to the gradation value data DI of the point to be processed and written to the address β1 of the RAM again.

As described above, the calculation of the recurrence formula shown in equation (7) is performed by the l vibline method by receiving and passing the address signals α and β one after another. And this αI,
Hiss 1 during operation from β to α, β
1+1 1÷1 Since storage accumulation (accumulation) of the shading value data D1 of the togerum data D is performed, the time required for the entire processing of one cycle can be shortened.

This one-cycle processing is performed in parallel by the DDA arithmetic circuits 18 to 18 constituting the DDA arithmetic unit 18. That is, when the data "■, empty, ■, empty, empty, ■, ■, empty" is input from the FIFO 17' as shown in FIG.
b), in the second cycle it becomes as shown in Figure <C>, in the third cycle it becomes as shown in Figure (d), and the same process is performed thereafter, and in the 8th cycle it becomes as shown in Figure <C>. (e)
become that way. Here, (α, β) in the same figure (a)
~(α, β) is the coordinate value (xy)1 4 pi' p1~(x
y) respectively, and DII to DI4 are the processing target points P1 to P4.
This is the gradation value data for each. Also, the same figure (
In b) to (e), θ to θ are rotation angles from the position (angle) θ' at which the On-1 initial values (α, β) were obtained, and are RAM in FIG. 2, respectively.

- Corresponds to RAM.

Next, neighborhood filtering after Hough transformation is completed will be described.

Now, when the intersection of the Hough curves is expressed on the ρ-θ plane, it is assumed that it becomes as shown in FIG. 13(a). Note that (a) in the same figure is a histogram obtained by weighting the intersection points that appear for each plane of the ρ-θ plane using the grayscale value data (change rate of 8 degrees) of the pixels (processing target points) in the original image. H is expressed by contour lines to make the explanation easier to understand, and does not necessarily match the histogram according to the present invention.

Here, the histogram is high at point p1 in FIG. It is assumed that the histogram is also high at p3. Focusing on the vicinity of point p1,
There is a point p4. It can be seen that high histogram portions also occur in p5 and the like. However, what is particularly important in image processing is to find points p1 to p3 that are far apart from each other. For example, point p1 is located on the shoulder line of the road, and point p
2 corresponds to the center line, and point p3 corresponds to the shoulder line of the curved road ahead. On the other hand, the point p4 near the maximum histogram point p1. P5 etc. often correspond to partial bends in the road shoulder line, etc.
In terms of image processing, this mainly corresponds to a noise component.

Therefore, the influence of such noise components is reduced by, for example, 8-neighborhood filtering. In other words, Fig. 13 (
An 8-neighborhood filter as shown in b) is prepared, and the histograms of the intersection points of the Hough curves for the area F-F9 are compared. Then, when F > F ˜F, FF holds true for the central area F5, the data of this area F5 is set as the data to be detected. Specifically, for example, when one unit (element work, rear) pixel on the ρ-θ plane is assigned to F1 to F9, the histogram numbers of intersection points are F -6, F2-8, F3- 4, ■ F-2, F5 mati 14, F 6-10
, F -7, F8-9, F9-8, F > F -F, F -F
Since the following holds true, the intersection point of F5 is set as the data to be detected. On the other hand, F, -8, F2-4, F3-3, F -1
4, F5-10, F6-7, F7-9, F8-
8, F9-2, since F5<F4, the area of F5 is not treated as data to be detected.

The above filtering process is performed using a neighborhood filter as shown in FIG. That is, a signal readout circuit 71 is attached to the histogram memory (RAM 34) configured as shown in FIG.
F9) is manually input to the shift register 74. Then, shift register F-F, F-F hist C-C
At the same time, the histogram data DM of F5 is input to all comparators. Then, when the histogram data in each area of the histogram memory 34 is DH1 to D as shown in the figure, the value of data DM5 is compared with other data DD and DD.

MI M4 MB M9 Then, when DM5 is at the maximum, the AND gate 75 outputs the peak signal as "H", and the data D at this time
M5 becomes the peak data.

By performing the above filtering process, the 13th
In figure (a), point p4. The high points of the second and third histograms, p2., are unaffected by the presence of p5. p
3 can be detected.

That is, if the above filtering were not performed, the next high histogram point after the first high histogram point p1 would be point p4. p5, and the point p2. which is desired to be obtained as the second and third high histogram points. p3 becomes N4 and the fifth high histogram point, making subsequent signal processing extremely difficult.

Next, the sorting process shown as step 118 in FIG. 3 will be explained in detail with reference to FIG. 15.

FIG. 15 is a flow chart thereof. First, for sorting processing, input memory M1 and comparison memory MM
Prepare l ~ MMn (n-1, 2r 3,4 ”・),
This is initialized (step 152).

Next, input data into the input memory Nf+ (step 154), and if this input data is present (step 15).
Only in step 6), the comparisons of steps 158, 162 and 166 are executed. If the input memory l M is larger, the contents are exchanged with the corresponding comparison memory M M (step 160° 164.168). Then, the comparison memory MM1'="Mn will eventually hold n pieces of input data in ascending order.

This is specifically shown in FIGS. 16 and 17. First, as shown in FIG. 16(a), four memories MIl=M14 are prepared as input memories and four memories MML""MM4 are prepared as comparison memories, and these are made bare to form a four-stage circuit. The circuit of each stage is shown in Figure 16(b).
As in, bare input memory M and comparison memory MM
, switching circuits 81 and 82, and a comparator 83 that controls them. When the histogram data input to the input memory M1 is larger than the data stored in the comparison memory MM, the output of the comparator 83 causes the switching circuit 81 to change as shown by the solid line in the figure, and the input data is transferred to the comparison memory MM. Stored. At the same time, since the switching circuit 82 is also shown as a solid line, the data stored in the comparison memory MM is sent to the next stage. On the other hand, when the manual data (input memory M,) is smaller than the data stored in the comparison memory MM, the switching circuit 81.8 is controlled by the comparator 83.
2 is as shown by the dotted line in the figure, the input data is sent as is to the next stage, and the contents of the comparison memory MM do not change.

In such a sorting section, it is assumed that the input data are 10 pieces of data "5, 7, 2, 8. 4. 9, 3, 1. 6. 8" as shown in FIG. 17(a). Then, the data stored in the memories MM1 to MM4 changes as shown by the arrows in FIG. Note that this sorting process may be executed by software. By executing the above series of processes, all steps of signal processing by the image processing apparatus according to the present invention are completed. Then, the approximate straight line of the curve connecting the processing target points of the original image is found using the above values of ρ and θ.

The present invention is not limited to the above embodiments, and various modifications are possible.

For example, the window setting shown in step 124 in FIG. 5 may be performed for a plurality of windows, and one of the windows may be processed preferentially.

Also, the window settings themselves are not necessarily necessary,
Hough transformation may be performed on the entire original image. However, in this case, the peripheral area of the original image indicated by the dotted line Q in Fig. 4(a) contains many noise components, so this area must be removed in advance when performing accurate image processing. It won't happen.

When obtaining a histogram of the intersection points of the Hough curves, only the concentration of the intersection points can be grasped as a histogram without superimposing shading value data regarding the rate of change in brightness (the value of the differentiated edged data). Furthermore, instead of converting the data into edge data through differentiation, the data corresponding to the luminance may be digitally processed as gradation value data, and then the histogram of the intersection of the :Hough curves may be obtained.

It is not necessary to calculate the rotational motion recurrence formula for the entire circumference (-circumference) of the approximate circle, but for 1/2 circumference, 1/2 circumference, etc.
It may be 4 rounds or 1/8 round. For example 1/4
By arranging DDAs in series to perform circumference calculations for 0≦θ and π/2 and π≦θ and 3π/2, it is possible to calculate other circumferences (π/2≦θ〈π, 3π/2≦θku2
π) can be immediately determined from these. Further, it is not necessarily necessary that the rotation angles are always the same, and it is not impossible to make them partially different.

The specific configuration of the DDA that performs the rotational motion recurrence formula is the 18th
It may be as shown in the figure. That is, six adders 51 to 56, three inverters 61 to 63, and two 1/2
It consists of 6 dividers 65 and 66. In addition, the symbol s in the figure,
s2°+l' S-3+g■ indicate a bit shift of m bits, 2m+1 bits, and -3m bits, respectively. According to this DDA, the above (8)
Expressions can be executed. Also, as shown in Figure 19,
It may be configured with two adders 51 and 52 and one inverter 61. In this way, the calculations of equation (5) ((a) in the same figure) and equation (6) ((b) in the same figure) described above can be executed.

〔Effect of the invention〕

As described above in detail, according to the present invention, a calculation unit can be easily formed by simply connecting DDA calculation circuits with the same configuration in series, and there is no need to refer to a memory table or the like during calculation. becomes significantly simpler and smaller. In addition, by configuring the arithmetic circuit to execute the rotational motion recurrence formula using the Vibrine method, it is possible to speed up the calculation.

[Brief explanation of the drawing]

FIG. 1 is a block diagram showing the overall configuration of an image processing device according to an embodiment of the present invention, FIG. 2 is a block diagram showing the configuration of the main parts of FIG. 1, and FIG. 3 shows the overall operation. 4 is a diagram illustrating edge detection of an example of an original image and an input pixel signal, FIG. 5 is a flowchart illustrating preprocessing of edged data, and FIG. 6 is a diagram illustrating a lookup table. 7 is a diagram explaining the Hough transformation in the embodiment of the present invention, FIG. 8 is a flowchart showing the calculation of the rotational motion recurrence formula in the embodiment, and FIG. 9 is a diagram showing the rotational motion recurrence formula. A circuit diagram showing the specific configuration of the DDA, FIG. 10 is a diagram explaining one cycle processing, FIG. 11 is a conceptual diagram of the histogram memory, and FIG. 12 is a diagram explaining the vibe line processing in the embodiment. 1st
Figure 3 is a diagram explaining the 8-neighborhood filtering process, the 14th
The figure shows the specific configuration of the neighborhood filter, FIG. 15 is a flowchart explaining the sorting process, FIG. 16 shows the specific configuration of the sorting section, and FIG. 17 specifically explains the sorting process. 18 and 19 are diagrams showing other examples of specific configurations of DDAs that perform rotary motion recurrence formulas, and FIG. 20 is a diagram explaining road recognition, and FIGS. Figure 23 shows the conventional Boug
It is a figure explaining h conversion. 1... Camera image, 2... Horizon, 3... Road,
4... Road shoulder line, 5... Center line, 11.
. . . Camera, 12 . Preprocessing section, 17'-F I Fo, 18-DDA calculation section, 1
8° to 18...DDA calculation circuit, 19...Neighborhood filter, 20...Sorting unit, 21.23...
・VME bus, 22...CPU, 31, 32.33・
...Flip-flop (F/F), 34...RAM
~RAM (histogram memory), 35...
・ADD to ADD (adder), 36... Buffer n-1 a, 37... DDA -DDA (DDA operation circuit n-1). Patent Applicant Honda Motor Co., Ltd. Representative Patent Attorney Yoshiki Hase Pretreatment Flowchart Figure 5 Input Data Input Data (a) LU
T7', CL (b) Operation of lookup table with LUT cLUT) Fig. 6 5h-1 Specific configuration of DDA Fig. 9 210 1 cycle processing 10th centroid N. RAM, RAMn-+Histogram Concept of memory Figure 11 Explanation of Hough transformation N) Figure 21 Explanation of Hough transformation (■) Figure 22

Claims (10)

[Claims] 1. In an image processing device that separates and extracts a curve connecting the processing target points from the characteristics of the distribution of a plurality of processing target points on the original image captured by the imaging means, an initial value is determined from the processing target points on the original image. An initial value calculation means for calculating; a DDA calculation means configured by connecting a plurality of DDA calculation elements of the same configuration in series and sequentially performing calculations from the initial value; and a calculation result of the DDA calculation means for the DDA calculation element. a storage means for storing information corresponding to the curve;
An image processing device comprising: means for extracting. 2. In an image processing device that derives an approximate straight line of a curve connecting a plurality of processing target points of an original image captured by an imaging means, the processing initial value coordinates ( α_0, β_0), the coordinates of one point on the approximate circle drawn in the α-β orthogonal coordinate system are (α_i, β_i), and the coordinates of the next point on the circle (α_i, β_i); α_i_+_1, β_i_+_1)
(where i is a positive integer), then α_i_+_1
= f_α (α_i, β_i, ε) β_i_+_1 = f_
DDA calculation means that sequentially calculates a rotational motion recurrence formula that is β (α_i, β_i, ε) from the initial value coordinates (α_0, β_0) for each predetermined rotation angle in a pipeline method;
Among the results sequentially calculated by this DDA calculation means, at least a storage means for storing the value of β_i, and a Hough curve for each of the plurality of processing target points obtained based on the stored contents of this storage means are stored. An image processing apparatus comprising: approximate straight line deriving means for deriving the approximate straight line from a point. 3. When the initial value calculation means sets the coordinates of the processing target point in the x-y orthogonal coordinate system to (x_p, y_p), α_0=-x_psinθ′+y_pcosθ′β_0
The coordinates of the initial value in the α-β orthogonal coordinate system (α_0, β_0) as =x_pcosθ′+y_psinθ′
3. The image processing apparatus according to claim 2, wherein the image processing apparatus calculates . 4. 3. The image processing apparatus according to claim 2, wherein the DDA calculation means is configured by connecting in series a plurality of calculation circuits that calculate the rotational motion recurrence formula for each rotation angle. 5. The rotational motion recurrence formula sets the rotational angle to ε=2^-^
When m, α_i_+_1=α_i(1-2^-^2^m^-^1
)-2^-^mβ_iβ_i_+_1=2^-^mα_
The image processing apparatus according to claim 2, wherein i+β_i(1-2^-^2^m^-^1). 6. 2. The storage means stores at least the value of β_i in correspondence with the rotation angle.
The image processing device described. 7. 3. The image processing apparatus according to claim 2, wherein the storage means stores gradation value data corresponding to the brightness of the processing target point or the rate of change thereof in the original image. 8. 7. The image processing apparatus according to claim 6, wherein the storage means stores the grayscale value data of the processing target point in the original image in correspondence with the rotation angle. 9. 3. The image processing apparatus according to claim 2, wherein the approximate straight line deriving means includes a neighborhood filter that performs neighborhood filtering processing on data regarding the intersection of the Hough curves. 10. 8. The image processing apparatus according to claim 7, wherein the approximate straight line deriving means includes a neighborhood filter that performs neighborhood filtering processing on data obtained by superimposing the gradation value data on data related to the intersection of the Hough curves.