JPH07131636A - Specified image detecting method and device for preventing specified image from being copied - Google Patents

Abstract

PURPOSE:To accurately detect any specified image such as paper money or the like and to prevent paper moneys or the like from being forged by illegally using a color copying machine or the like by working those image data. CONSTITUTION:The R chromaticity of each picture element in an input image from a device 100 is calculated by a block 102. A small area to be the candidate of the specified image is extracted based on R chromaticity data by a block 104 and concerning this small area, and R chromaticity histogram is prepared by a block 106. The prepared R chromaticity histogram is matched with the R chromaticity histogram of the specified image stored in a dictionary 109 by a block 108, and the specified image is detected and decided. When the specified image is detected, the RGB data of the input image are converted and outputted by a block 500 but when no specified image is detected, those data are outputted as they are.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technique for detecting a specific image in a color image input by a color scanner or the like, and a technique for preventing forgery of banknotes or the like by misuse of a color copying machine or the like.

[0002]

2. Description of the Related Art Not only in the field of image recognition, but also in the field of image databases, color copiers, color printers, and other devices that handle images, it is often necessary to detect a specific image.

For example, due to the dramatic improvement in performance of devices such as color copiers, color scanners, and color printers in recent years, there is an increasing need to take measures to prevent counterfeiting of banknotes and securities that have been abused. In order to realize this, it is necessary to detect a specific image such as a bill.

Conventionally, when detecting a color specific image,
As disclosed in Japanese Patent Laid-Open No. 4-207465,
For example, a method using the degree of coincidence of the spread in the color space (hatched portions in FIGS. 4 and 5 of the publication) is used.

[0005]

However, as in the above-mentioned method, it is possible to detect in a method in which only the presence or absence of the color information necessary for the detection exists and the information of the position where the color exists is not considered at all. When an image having a color tone similar to that of the specified image is input, there is a high risk of erroneously detecting this as the specific image.

Further, in the above method, the detection process is performed by using only the spread of the color space indicated by the dictionary of the specific image as a feature (the degree of similarity is determined by the degree of coincidence in the color space). , If the image has the same spread in the color space, even if the color distribution inside the image is different from the specific image,
False detection as a specific image.

An object of the present invention is to provide a method for discriminating between an image having a spread in a color space and a specific image and detecting the specific image with high accuracy.

Another object of the present invention is to provide a method for accurately detecting a plurality of specific images having a similar spread in the color space.

Another object of the present invention is to provide a device for preventing forgery of banknotes and the like by misuse of a color scanner or a color copying machine.

[0010]

According to a first aspect of the present invention, when a specific image in an input image is detected using color information, the input image is divided into small areas, and the specific image is generated for each small area. It is characterized by determining whether or not there is a possibility of being included, extracting color information from a small area determined as possible, and using the extracted color information for detection determination of a specific image. It is a thing.

According to a second aspect of the present invention, in the method of the first aspect, the difference between the maximum value and the minimum value of the color information is calculated for each small area, and based on the magnitude relation between this difference and a certain threshold value. It is characterized by determining whether there is a possibility that a specific image is included.

According to a third aspect of the present invention, in the method of the second aspect, the threshold value and the difference between the maximum value and the minimum value of the color information are both above the threshold value and below the threshold value. In this case, whether or not it should be determined that the specific image may be included is controlled in association with the specific image that is the detection target.

According to a fourth aspect of the present invention, in the method of the third aspect, the difference between the threshold value information and the maximum value and the minimum value of the color information is not less than the threshold value and not more than the threshold value. It is characterized in that information specifying in which case it should be determined that the specific image may be included is prepared for each specific image to be detected.

According to a fifth aspect of the present invention, in the method of the third aspect, the specific images to be detected are grouped, and threshold value information and maximum and minimum values of color information are set for each group. It is characterized by preparing information for designating whether the difference between the threshold values is greater than or equal to the threshold value and less than or equal to the threshold value, and it is determined that the specific image may be included.

According to a sixth aspect of the present invention, in the method of the first aspect of the invention, it is checked for each small area whether or not there is color information not included in the specific image. It is characterized in that the area is judged to have a possibility of containing a specific image.

The invention of claim 7 is an apparatus for preventing the duplication of a specific image such as a banknote, and the copying of an input image is prohibited by the method of the invention of any one of claims 1 to 6. Means for performing detection processing of specific images such as bills,
When the specific image is detected by the means, the input image data is processed and then output.

[0017]

When the specific image is detected using the color distribution, the color information of the part other than the specific image adversely affects the detection process.

According to the methods of the first to sixth aspects of the invention, only the color information of the area that may be the specific image to be detected is extracted and used for detecting the specific image. That is, according to the present invention, the color information extracted in consideration of the information of the position where the color information exists is used for detecting the specific image. Therefore, by eliminating the adverse effects of the above-mentioned extra color information, it is possible to identify an image having a similar hue to the specific image, or an image having a similar spread in the color space and having an internal color distribution different from that of the specific image. The specific image can be detected accurately by distinguishing it from the image.

The distribution of the difference between the maximum value and the minimum value of the color information in the small area is biased toward the smaller side in an image with many solid portions, as shown in a concrete example below, and, conversely, it has a fine texture. It is considerably different depending on the nature of the image, such that the large number of specific images are biased toward the larger side. Therefore, according to the inventions of claims 2 to 5, the threshold value relating to the difference between the maximum value and the minimum value of the color information and the judgment condition based on the magnitude relation thereof are determined according to the property of the specific image to be detected. By appropriately controlling, a small area that may include the specific image can be accurately determined, and as a result, the detection accuracy of the specific image can be increased.

In particular, according to the invention of claim 4, since information for the above control is held for each individual specific image, the regions of various specific images having different properties are extracted with high accuracy. , Each specific image can be detected accurately.

On the other hand, according to the fifth aspect of the present invention, two or more specific images having similar extraction conditions for the region that may include the specific image are grouped into one group, and the region extraction condition is set. By sharing the information for control, the memory capacity for storing the information can be reduced, and the time for the reference can be reduced to shorten the processing time.

According to the invention of claim 6, in the case of one specific image or a plurality of specific images having similar properties,
Since the information for controlling the area extraction of the specific image can be reduced without lowering the detection accuracy, it is possible to reduce the memory capacity for the storage and speed up the processing.

On the other hand, according to the seventh aspect of the present invention, even if an attempt is made to input and copy a specific image for which copying of banknotes or securities is prohibited by an image scanner or a scanner section of a color copying machine, The data is output after being processed into data of an image different from the original image. Therefore, if the present invention is applied to an image scanner, a color copying machine or the like, forgery of banknotes can be surely prevented.

[0024]

Embodiments of the present invention will be described below with reference to the drawings.

<First Embodiment> FIG. 1 shows the configuration of an apparatus used in this embodiment. In FIG. 1, a color image input device (color scanner) 100 inputs a color image as RGB data.

In this embodiment, an R chromaticity histogram is used as the amount of information for image identification. This is for simplifying the description, and a multidimensional feature amount (for example, R and G chromaticity histograms) can also be used.

In the R chromaticity calculator 102, the input RGB
From the data, the chromaticity r of R for each pixel is calculated by the following equation. r = 256 × R / (R + G + B) Therefore, the chromaticity of R takes a value from 0 to 256. The calculated R chromaticity data is stored in the effective small area extracting unit 104 and the effective small area R chromaticity histogram creating unit 1.
It is input to 06.

The effective small area extracting unit 104 divides the image to be processed, that is, the input image, into small areas, here, small areas of 64 × 64 pixels, and a specific image may be included in each small area. It is determined whether or not it is a certain area (effective small area), and the determination result is notified to the in-effective small area R chromaticity histogram creating unit 106. The method of determining the effective small area will be described later.

R chromaticity histogram creation unit 1 in the effective small area
06 creates an R chromaticity histogram in the effective small area from the R chromaticity data in the small area determined to be the effective small area, and outputs the data to the matching unit 108. The matching unit 108 performs detection matching of the specific image by matching the R histogram in the effective small area with the specific image dictionary 109.

FIG. 2 shows an example of an image to be processed in this embodiment. This image contains two specific images A and B to be detected.

FIG. 3 shows an example of the contents of the specific image dictionary 109 used in this embodiment. The dictionary shown here consists of five fields 110, 112, 114, 116, 118. The contents of each field will be described below.

The field 110 (first byte) stores the R chromaticity difference threshold value in the small area used by the effective small area extraction unit 104 for determining the effective small area. In this example, this value is set to 10.

A field 112 is a flag for designating the magnitude relationship of the R chromaticity difference (described later) in the small area with respect to the R chromaticity difference threshold value to determine the effective small area. The R difference effective direction in a certain small area is stored.
In this example, since this flag is set to 0, the small area is determined to be an effective small area when the R chromaticity difference in the small area is equal to or less than the R chromaticity difference threshold in the small area. If the flag is set to 1, when the R chromaticity difference in the small area is equal to or less than the R chromaticity difference threshold in the small area, it is determined to be the effective area.

In general, when detecting a specific image having many solid portions, it is possible to efficiently extract the effective small area by setting the R chromaticity difference effective direction in the small area to 0. on the other hand,
When detecting a specific image with many fine textures, the effective small area can be efficiently extracted by setting the R chromaticity difference effective direction in the small area to 1.

Fields 116 and 118 store the R chromaticity histograms of the specific images A and B, which are obtained in advance. An example of each histogram is shown in FIG.

The field 144 stores a specific image detection distance threshold value. The matching unit 108 uses the R of the specific image A or B in the field 116 or 118.
Insufficient frequency for each value of the R chromaticity histogram created from the processing target image with respect to the chromaticity histogram (degree of difference)
, It is determined that the specific image A or B is detected when the specific image detection distance threshold is smaller than this.

The processing contents will be described more specifically below.
In the effective small area extraction unit 104, as shown in FIG.
The processing target image is divided into 64 × 64 pixel small areas 131, and 64 representative pixels 133 are arranged every 8 pixels for each small area.
Choose. The difference between the maximum value and the minimum value of the R chromaticity of these 64 representative pixels is calculated, and this is set as the R chromaticity difference value in the small area.
Then, the R chromaticity difference value in the small area and the specific image dictionary 1
It is determined whether or not it is an effective small area according to the magnitude relationship with the small area R chromaticity difference threshold value in the field 110 of 09 and the small area R chromaticity difference effective direction in the field 112. In this embodiment, the value of the field 110 is 10,
Since the value of the field 112 is 0, if the R chromaticity difference value in the small area is 10 or less, it is determined to be a valid small area, and if not, it is determined to be an invalid small area.

FIG. 7 shows the extraction result of the effective small area for the image to be processed in FIG. The colored portion represents an effective small area, and the uncolored portion represents an invalid small area. Since the background portion of the image has a light texture, the R chromaticity difference in the small area does not become 10 or less, so that it becomes an invalid small area.

For each effective small area extracted in this way, the R chromaticity histogram creation unit 106 in the effective small area
In, the R chromaticity histogram is created using the R chromaticity data of each pixel in the area and input to the matching unit 108.

In the matching unit 108, the frequency of each dimension of the input R chromaticity histogram in the effective small area is insufficient (the degree of difference) with respect to the R chromaticity histograms of the specific images A and B.
To calculate. Mathematically, this calculation is represented by the following equation.

[0041]

[Equation 1]

When the difference degree Dest calculated for the specific image A or B is smaller than the specific image detection distance threshold value in the field 114 of the specific image dictionary 109, here 64, the specific image A or B is detected. It is determined that it has been done.

FIG. 8 is an explanatory diagram of this matching.
An example of the R chromaticity histogram in the effective small area created from the image to be processed is shown in the upper part of FIG. The middle tier and the lower tier show the R chromaticity histograms (hatched portions) of the specific images A and B and the upper tier in a superimposed manner. If the degree of difference from the specific image A is 33 (pixels) as in the description in the middle row, this is smaller than 64, and it is determined that the specific image A has been detected.
On the other hand, as described in the lower part, when the degree of difference with the specific image B is 58, this is smaller than 64, so it is determined that the specific image B has been detected.

When the image shown in FIG. 2 is processed as described above, an effective small area which is a candidate area of a specific image as shown in FIG. 7 is detected, and the distribution of color information in the area is used. The specific images A and B are detected. When the specific image is detected using the color distribution, the extra color information about the portion that cannot be the region of the specific image adversely affects the detection accuracy. According to the present embodiment, since such extra color information is not used, its adverse effect can be eliminated and the specific image can be accurately detected.

The R chromaticity difference threshold in the small area and the R chromaticity difference effective direction in the small area have been described above, and a specific method for determining them will be described with reference to FIG.

For each of the specific images A and B, the same processing as that in the effective small area extraction unit 104 is performed to obtain 64
It is divided into small regions of x64 pixels, the R chromaticity difference in the small regions is detected, and a histogram thereof is created. In this way, it is assumed that for the specific images A and B, the R chromaticity difference histogram in the small area as shown in the upper and lower rows of FIG. 5 is obtained.

The shaded area in each histogram is
It is a continuous area having a frequency other than 0 and having the largest number of small areas therein. This is called the "maximum continuous area (RSUBHIST)"
Call. Then, the small region R chromaticity difference threshold RTH and the small region R chromaticity difference effective direction RD can be determined as follows.

(1) (256-max (RSUBHIST)) <min (RS
UBHIST) is satisfied, RTH = min (RSUBHIST) RD = 1 (2) If the above condition is not satisfied, RTH = max (RSUBHIST) RD = 0 In the example shown in FIG. Maximum R chromaticity difference ma in the region
x (RSUBHIST) is 10, the minimum R chromaticity difference min (RSUBHIS
Since T) is 0, the above condition does not hold. Therefore,
By (2), RTH = 10 and RD = 0 are set.

<Embodiment 2> In this embodiment as well, since the apparatus configuration shown in FIG. 1 is used as in the case of Embodiment 1, the description thereof is omitted. Further, the feature amount used for detecting the specific image is also the same as that in the first embodiment. The difference from the first embodiment is that each specific image has a small region R chromaticity difference threshold value and a small region R chromaticity difference effective direction in the specific image dictionary 109. is there.

FIG. 9 shows a specific image dictionary 1 in this embodiment.
The content of 09 is illustrated. In FIG. 9, 200 is a field in which a specific image detection distance threshold value is stored. Two
Reference numerals 02, 204, and 206 are fields in which the small region R chromaticity difference threshold value, the small region R chromaticity difference effective direction, and the R chromaticity histogram for the specific image A (see FIG. 2) are stored, respectively. . Also, 208, 210 and 212 are
This is a field for storing the R chromaticity difference threshold value in the small area, the R chromaticity difference effective direction in the small area, and the R chromaticity histogram for the specific image C (see FIG. 2).

FIG. 10 shows histograms of the R chromaticity differences in the small areas of the specific images A and C, respectively. As can be seen from the histogram of the specific image A shown in the upper part of FIG. 10 and the histogram of the specific image C shown in the lower part, the deviation degrees of the respective histograms are significantly different. That is, since the specific image A is an image with many solid portions, the R chromaticity difference histogram is biased toward the side with the smaller R chromaticity difference.
This means that a small area having a large R chromaticity difference can be determined as an invalid small area. On the other hand, since the specific image C is an image having many fine texture portions, the R chromaticity difference histogram is biased toward the side where the R chromaticity difference is large.
A small area having a small chromaticity difference can be determined as an invalid small area.

In this case, the capacity of the specific image dictionary 109 increases, but as in the present embodiment, the R chromaticity difference threshold within the small region and the R chromaticity difference within the small region for each specific image. When the effective directions are separately determined and stored in the specific image dictionary 109, the specific image can be detected more accurately than when they are shared.

In the case of this example, according to the method described in the first embodiment, the specific image A is
The R chromaticity difference threshold in the small region is 10 (the maximum R chromaticity difference in the maximum continuous region shaded in the histogram in the upper part of FIG. 10), and the R chromaticity difference effective direction in the small region is 0 (in the small region R When the chromaticity difference is equal to or smaller than the R chromaticity difference threshold value in the small area, it is set to the effective small area). On the other hand, regarding the specific image C, the R chromaticity difference threshold value in the small area is 2
00 (minimum R chromaticity difference of the maximum continuous area shaded in the histogram in the lower part of FIG. 10), R chromaticity difference in small area Effective direction is 1 (R chromaticity difference in small area is R chromaticity difference in small area) When the threshold value is equal to or more than the threshold value, the effective small area is set).

The specific image detection process is as follows. The effective small area extraction unit 104 divides the image to be processed (FIG. 2) into small areas of 64 × 64 pixels and selects 64 representative pixels at every 8 pixels for each small area, as in the first embodiment. Then, the difference between the maximum value and the minimum value of the R chromaticity of these representative pixels is calculated as the R chromaticity difference within the small area. Then, the specific image dictionary 1
09 field 202 has value 10 and field 204
Since the value of is 0, a small area having an R chromaticity difference within a small area of 10 or less is determined to be an effective small area of the specific image A. The value of the field 208 of the specific image dictionary 109 is 200,
Since the value of the field 210 is 1, a small area having an R chromaticity difference within a small area of 200 or more is determined as an effective small area of the specific image C.

FIG. 11 shows the effective small area of the specific image C extracted from the image to be processed in FIG. The colored grid is the effective small area, and the white grid is the ineffective small area. For the effective small area of the specific image A, refer to FIG. 7.

R Chromaticity Histogram Creating Unit 1 in Effective Small Area
In 06, for each of the specific images A and C, the R chromaticity histogram in the small region is created from the R chromaticity data (input from the R chromaticity calculation unit 102) of the pixels in the effective small region, and the matching unit 108 is created. Give to. Matching unit 108
Then, the degree of difference between the R chromaticity histogram of the specific image A and the R chromaticity histogram in the field 206 of the specific image dictionary 109 is obtained, and when this is smaller than the specific image detection distance threshold value (here, 64), It is determined that the specific image A is detected. Further, the degree of difference between the R chromaticity histogram of the specific image and the R chromaticity histogram in the field 212 of the specific image dictionary 109 is obtained, and when the difference is smaller than the specific image detection distance threshold value (here, 64), the specific image detection distance threshold value is specified. It is determined that the image C is detected.

FIG. 12 is an explanatory diagram of this matching. The upper part of FIG. 12 shows the R chromaticity histogram in the effective small area of the specific image C created from the processing target image. The R chromaticity histogram in the effective small area of the specific image A is shown in the upper part of FIG. 8, and is not shown in this drawing. In the middle section, the R chromaticity histogram for the specific image A (hatched portion) stored in the specific image dictionary 109 is shown superimposed on the R chromaticity histogram in the effective small area of the created specific image A. . In the lower part, the R chromaticity histogram for the specific image C in the dictionary (hatched portion) is shown in an overlapping manner with the R chromaticity histogram in the effective small area of the specific image C shown in the upper part.

As in the explanation in the middle, the degree of difference regarding the specific image A is 33 (pixels), which is smaller than the specific image detection distance threshold 64, so it is determined that the specific image A has been detected. On the other hand, as shown in the explanation at the bottom, the specific image C
The degree of difference with respect to is 0, which is smaller than 64, and thus it is determined that the specific image C is also detected.

<Embodiment 3> In this embodiment as well, since the apparatus configuration shown in FIG. 1 is used as in the case of Embodiment 1, the description thereof is omitted. The feature amount used for detecting the specific image is also the same as that in the first embodiment.

In this embodiment, three types of specific images A, B and C are used.
(See FIG. 2) is the detection target, and the specific image dictionary 109
Shows the contents as shown in FIG. The difference in the dictionary contents from the first embodiment is that the R chromaticity difference threshold value in the small area and the R chromaticity difference effective direction in the small area are specified to have a similar distribution state of the R chromaticity difference in the small area. This is the point that each image has.

Here, description will be made with reference to FIG. FIG. 14 shows histograms of the R chromaticity differences in the small areas of the specific images A, B, and C, respectively. The shaded area is the maximum continuous area.

It is understood that the R chromaticity difference histogram of the specific image C shown in the upper part of FIG. 14 and the R chromaticity difference histogram of the specific image A shown in the middle part are greatly different in deviation degree. . Since the specific image A is an image having many solid portions, its R chromaticity difference histogram is biased toward the side with a small R chromaticity difference, and therefore, a small area with a large R chromaticity difference can be determined as an invalid small area. On the other hand, since the specific image C is an image having many fine texture portions, the R chromaticity difference histogram is biased toward the side with a large R chromaticity difference. Therefore, a small area with a small R chromaticity difference is regarded as an invalid small area. You can judge.

In the case of such a specific image, the dictionary capacity increases and the processing time becomes slower, but for each specific pixel,
It is possible to accurately detect those specific images by separately storing the R-chromaticity difference threshold in the small region and the R-chromaticity difference effective direction in the small region in the dictionary.

On the other hand, in the case of the specific images A and B, the deviation degrees of the R chromaticity difference histograms shown in the middle and lower parts of FIG. 14 are very similar, and the R chromaticity difference in each small area is different. The threshold value and the R chromaticity difference effective direction in the small area have the same value. For such a specific image, common processing can be performed in creating the R chromaticity histogram in the effective small area. Therefore, the R chromaticity difference threshold in the small area and the R chromaticity difference in the small area effective direction It turns out that it is more rational to have in common in the dictionary.

From this point of view, the specific image dictionary 109
The contents of are configured. The contents of the specific image dictionary 109 will be described below with reference to FIG.

Reference numeral 300 denotes a field for the specific image detection distance threshold value, which has a value of 64 as in the above-mentioned embodiments.

Reference numerals 302 to 308 are fields related to the specific image C. The field 302 stores the number of specific images to which the common small region R chromaticity difference threshold value and the common small region R chromaticity difference effective direction are applied. Since only the specific image C is applicable here, 1 is stored. The field 304 and the field 306 store the R chromaticity difference threshold value in the small area and the R chromaticity difference effective direction for the specific image C. In this example, it is understood from the histogram in the upper part of FIG. 14 and the description of each of the embodiments. As can be seen, the respective values are 200 and 1. The field 308 stores the small region R chromaticity histogram of the specific image C.

Reference numerals 310 to 317 are fields related to the specific images A and B. The field 261 stores a specific image to which the common small area R chromaticity difference threshold and the common small area R chromaticity difference effective direction are applied, here, the number of specific images A and B, that is, 2. It A field 312 and a field 314 store a small region R chromaticity difference threshold value and a small region R chromaticity difference effective direction that are commonly applied to the specific images A and B. As can be understood from the histogram shown in the lower part, the values of 10 and 0 are stored. Then, the R chromaticity histogram in the small area of the specific image A is stored in the field 316, and the specific image B is stored in the field 318.

In the effective small area extraction unit 104, the image to be processed is 64 × 64 as in the case of the first embodiment.
Divide into small areas of pixels, and for each small area, 6 every 8 pixels
Four representative pixels are selected, and the difference between the maximum value and the minimum value of the R chromaticity is set as the R chromaticity difference in the small area. Then, according to the contents of the specific image dictionary 109, since the values of the fields 304 and 306 are 200 and 1 for the specific image C, if the R chromaticity difference in the small area is 200 or more, the effective smallness of the specific image C is small. Judge as an area. Further, since the values of the fields 312 and 314 are 10 and 0 for the specific images A and B, when the R chromaticity difference in the small region is 10 or less, it is an effective small region of the specific images A and B. judge. Refer to FIG. 11 for the specific image C and FIG. 7 for the specific images A and B for the extraction result of the effective small area.

Then, the R chromaticity histogram in the effective small area of each specific image is created in the effective small area R chromaticity histogram creating section 106, and the matching section 108 is created.
Give to. In the matching unit 108, the R chromaticity histogram in the effective small area of the given specific image C and the specific images A and C, and the R chromaticity histogram in the corresponding field 308 and field 316 or 318 of the specific image dictionary 109. Are calculated, and when the difference is smaller than the value in the field 300, it is determined that each specific image is detected. For the degree of difference, see FIGS. 8 and 12.

<Embodiment 4> Since the apparatus configuration shown in FIG. 1 is used in this embodiment as in the case of Embodiment 1, the description thereof will be omitted. The feature amount used for detecting the specific image is also the same as that in the first embodiment.

In the present embodiment, only one type of specific image C (see FIG. 2) is detected, and the specific image dictionary 109 has the contents shown in FIG. The difference in the dictionary contents from the first embodiment is that there is no R-chromaticity difference threshold in the small region and R-chromaticity difference effective direction in the small region (this is not necessary in the present embodiment). That is, it is composed only of the field 400 of the specific image detection distance threshold value (64 in this case) and the field 402 of the R chromaticity histogram in the small area of the specific image C. FIG. 16 shows the R chromaticity histogram in the small area of the specific image C to be detected.

The specific image detection processing is as follows. In the effective small area extraction unit 104, as in the first embodiment, 64
64 representative points are selected for each small area of x64 pixels. When the representative pixel includes even one pixel having the R chromaticity of which the frequency (number of pixels) of the histogram shown in FIG. 16 is 0, the small area is determined to be an invalid small area, and When such a representative pixel is not included at all, it is determined as an effective small area. Therefore, in the case of the processing target image of FIG. 2, the result of effective small area extraction is as shown in FIG. In FIG. 17, the white grid is an ineffective small area.

R Chromaticity Histogram Creating Unit 1 in Effective Small Area
In 06, the R of the pixel in the small area determined to be the effective small area
An R chromaticity histogram is created from the chromaticity data. The matching unit 108 calculates the degree of difference between the created R chromaticity histogram and the histogram stored in the field 402 of the specific image dictionary 109, and when the difference is smaller than the value of the field 400, that is, 64, the specific image is calculated. It is determined that C is detected.

<Embodiment 5> This embodiment is intended to prevent electronic copying of a specific image for which copying of banknotes or securities is prohibited. FIG. 18 shows an example of the device configuration. 18, the same reference numerals as those in FIG. 1 mean the same parts. Only the image conversion unit 500 is added.

That is, in the components from the R chromaticity calculation unit 102 to the specific image dictionary 109, the above-mentioned first, second,
Through the process described in 3 or 4, the image input from the color image input device 100 is used as a target to detect a specific image in which copying is prohibited such as banknotes. For example, when an image including the image D (specific image) of a banknote as shown in FIG. 19 is input, the matching unit 108 issues a notification of detection of the banknote image D. Naturally, the information for the specific image D is the dictionary 10
Stored in 9.

When this detection notification is issued, the image conversion unit 500
Is the R of the image input from the color image input device 100.
The GB data is subjected to image conversion and output. For example, RG
B A specific conversion is performed for each color. As a result, for example, the RGB of an image having a color tone different from that of the input image as shown in FIG.
The data is output. Therefore, even if this output data is printed by a color printer or the like, the bill cannot be duplicated.

On the other hand, when the detection notification is not issued from the matching unit 108, the image conversion unit 500 outputs the RGB data of the input image as it is, so that the input image can be faithfully reproduced.

As described above, this embodiment uses a color copying machine,
If applied to color scanners, color printers, etc.,
It is possible to prevent forgery of banknotes and the like that misuse these.

[0080]

As described above, according to the present invention, the following effects can be obtained.

According to the methods of the first to sixth aspects of the present invention, only the color information of the area which may be the specific image to be detected is extracted and used for detecting the specific image. That is, according to the present invention, the color information extracted in consideration of the information of the position where the color information exists is used for detecting the specific image. Therefore, the adverse effect of the extra color information of the portion other than the specific image is eliminated, and the image having a similar hue to the specific image or the image having a similar spread in the color space and having an internal color distribution different from the specific image Can be distinguished from the specific image, and the specific image can be detected with high accuracy.

It is said that the distribution of the difference between the maximum value and the minimum value of the color information in the small area is biased toward the small side in an image with many solid portions, and is conversely biased toward the large side in a specific image with many fine textures. As such, it varies considerably depending on the nature of the image.

Therefore, according to the inventions of claims 2 to 5, depending on the property of the specific image to be detected, the threshold value relating to the difference between the maximum value and the minimum value of the color information and its magnitude relation are used. By controlling the judgment conditions appropriately,
A small area that may include the specific image can be accurately determined, and as a result, the detection accuracy of the specific image can be improved.

According to the invention of claim 4, since the information for the above control is held for each individual specific image, the regions of various specific images having different properties are extracted with high accuracy, and each region is extracted. The specific image can be detected accurately. Further, according to the invention of claim 5, two or more specific images having similar extraction conditions of an area that may include the specific image are grouped into one group, and the area extraction condition is controlled. By sharing the information of (1), the memory capacity for storing the information can be reduced, and the time required for the reference can be reduced to shorten the processing time.

According to the sixth aspect of the invention, in the case of one specific image or a plurality of specific images having similar properties, information for controlling the area extraction of the specific image is obtained without lowering the detection accuracy. Since this can be reduced, it is possible to achieve reduction of the memory capacity for saving and speeding up of processing.

According to the seventh aspect of the present invention, even if a specific image in which copying of banknotes or securities is prohibited by an image scanner or a scanner unit of a color copying machine and the like is copied, the input image data is , Is output after being processed into data of an image different from the original image. Therefore,
If the present invention is applied to an image scanner, a color copying machine, etc., forgery of banknotes can be surely prevented.

[Brief description of drawings]

FIG. 1 illustrates an example of a device configuration according to first to fourth embodiments.

FIG. 2 shows an example of an input image that is a processing target of the first to fourth embodiments.

FIG. 3 shows an example of contents of a specific image dictionary in the first embodiment.

FIG. 4 shows R chromaticity histograms of specific images A and B for the purpose of explaining the first embodiment.

5A and 5B show R chromaticity difference histograms in small areas of specific images A and B for explaining the first embodiment.

FIG. 6 is an explanatory diagram of division of a small area and representative pixels in the small area.

7A and 7B show extraction results of effective small areas of specific images A and B for explaining the first embodiment.

FIG. 8 is an explanatory diagram of matching of the R chromaticity histogram in the small area in the first embodiment.

FIG. 9 shows an example of contents of a specific image dictionary in the second embodiment.

FIG. 10 shows a small region R chromaticity difference histogram of specific images A and C for explaining the second embodiment.

FIG. 11 shows the extraction result of the effective small area of the specific image C for explaining the second embodiment.

FIG. 12 is an explanatory diagram of matching of the R chromaticity histogram in the small area according to the second embodiment.

FIG. 13 shows an example of contents of a specific image dictionary for explaining the third embodiment.

FIG. 14 is a specific image A, B, C for explaining the third embodiment.
7 shows an R chromaticity difference histogram in the small area of FIG.

FIG. 15 shows an example of contents of a specific image dictionary for explaining a fourth embodiment.

FIG. 16 shows an R chromaticity histogram in a small area of the specific image C for explaining the fourth embodiment.

FIG. 17 shows the extraction result of the effective small area for explaining the fourth embodiment.

FIG. 18 illustrates an example of a device configuration according to a fifth embodiment.

FIG. 19 shows an example of an input image for explaining the fifth embodiment.

FIG. 20 shows an example of an output image for explaining the fifth embodiment.

[Explanation of symbols]

100 Color Image Input Device (Color Scanner) 102 R Chromaticity Calculation Unit 104 Effective Small Region Extraction Unit 106 Effective Small Region R Chromaticity Histogram Creation Unit 108 Matching Unit 109 Specific Image Dictionary 110 Small Region R Chromaticity Difference Threshold Value field 112 Field of R chromaticity difference effective direction in small area 114 Field of specific image detection distance threshold 116 Field of R chromaticity histogram of specific image A 118 Field of R chromaticity histogram of specific image B 131 Small area 133 Representative pixel in small area 200 Field of specific image detection distance threshold 202 Field of R chromaticity difference threshold in small area of specific image A 204 Field of R chromaticity difference effective direction in small area of specific image A 206 Field of R Chromaticity Histogram of Specific Image A 208 Specific Image C Field of R chromaticity difference threshold in small area 210 Field of R chromaticity difference effective direction in small area of specific image C 212 Field of R chromaticity histogram of specific image C 300 Field of specific image detection distance threshold 302 , 310 same R chromaticity difference threshold field of specific image number 304 field of R chromaticity difference threshold value in small area of specific image C 306 field of R chromaticity difference effective direction in small area of specific image C 308 specific Field of R chromaticity histogram of image C 312 Field of R chromaticity difference threshold value in small area of specific images A and B 314 Field of R chromaticity difference effective direction of small area of specific images A and B 316 Specific image A R chromaticity histogram field of 318 Specific image B R chromaticity histogram field of 400 Specific image detection distance threshold Field 402 Field of R chromaticity histogram of specific image C 500 Image conversion unit

Claims (7)

[Claims] 1. A method for detecting a specific image in an input image using color information, wherein the input image is divided into small areas, and it is possible that the specific image is included in each small area. A specific image detecting method, characterized in that the color information is extracted from a small area determined to be possible, and the extracted color information is used for detection determination of the specific image. 2. A difference between a maximum value and a minimum value of color information is calculated for each small area, and it is determined whether or not a specific image is included based on a magnitude relationship between the difference and a certain threshold value. The specific image detecting method according to claim 1, wherein 3. A specific image may be included when the threshold value and the difference between the maximum value and the minimum value of the color information are both above the threshold value and below the threshold value. The specific image detecting method according to claim 2, wherein whether or not to make a determination is controlled in association with a specific image that is a detection target. 4. The specific image may be included when the difference between the threshold value information and the maximum value and the minimum value of the color information is greater than or equal to the threshold value and less than or equal to the threshold value. 4. The specific image detecting method according to claim 3, wherein information specifying whether or not to determine that there is is prepared for each specific image to be detected. 5. A specific image to be detected is grouped, and for each group, threshold information and a difference between the maximum value and the minimum value of color information is greater than or equal to the threshold value and less than or equal to the threshold value. 4. The specific image detecting method according to claim 3, wherein information is provided to specify in which case it should be determined that the specific image may be included. 6. It is checked whether or not there is color information that is not included in the specific image for each small area, and there is a possibility that the specific image is included in the small area where the color information does not exist. The specific image detection method according to claim 1, wherein 7. A method for detecting a specific image such as a banknote for which copying is prohibited for an input image by the method according to any one of claims 1 to 6; A specific image duplication preventing apparatus, comprising: means for processing data of an input image and outputting the data when detected.