Source: https://patents.google.com/patent/JP4538507B2/en
Timestamp: 2019-11-17 18:43:12
Document Index: 468070669

Matched Legal Cases: ['art 2', 'art 3', 'art 12', 'art 13', 'art 14', 'art 15', 'art 16', 'art)\n101']

JP4538507B2 - Image collation method, image collation apparatus, image data output processing apparatus, program, and storage medium - Google Patents
Image collation method, image collation apparatus, image data output processing apparatus, program, and storage medium Download PDF
JP4538507B2
JP4538507B2 JP2008120256A JP2008120256A JP4538507B2 JP 4538507 B2 JP4538507 B2 JP 4538507B2 JP 2008120256 A JP2008120256 A JP 2008120256A JP 2008120256 A JP2008120256 A JP 2008120256A JP 4538507 B2 JP4538507 B2 JP 4538507B2
JP2008120256A
JP2009271655A (en
2008-05-02 Application filed by シャープ株式会社 filed Critical シャープ株式会社
2008-05-02 Priority to JP2008120256A priority Critical patent/JP4538507B2/en
2009-11-19 Publication of JP2009271655A publication Critical patent/JP2009271655A/en
2010-09-08 Publication of JP4538507B2 publication Critical patent/JP4538507B2/en
The present invention relates to an image matching method, an image matching device, an image data output processing device, a program, and a storage medium for an image (document image) including characters and codes.
There is an image data output processing device that performs output processing such as copying, data transmission, and filing on input input image data. In such an image data output processing apparatus, various document image matching techniques for determining similarity between images have been conventionally used.
As an example of use, for example, the feature value of the input document image is extracted from the image data of the input document image (input document image), and this is compared with the feature value of the registered document image that has already been registered. The similarity between the input document image and the registered document image is determined, and if they are similar, output control for restricting the output process of the image data of the input document or processing under a predetermined condition is performed. It has been proposed.
For the determination of the similarity between images, for example, a keyword is extracted from an image using an OCR (Optical Character Reader), the similarity of the image is determined based on the extracted keyword, and the image for which the similarity is determined is determined by the ruled line. A method for determining the similarity of images by extracting features of ruled lines only for a certain form image, replacing character strings of image data with points, and obtaining the positional relationship of points (feature points) as feature amounts A method for determining the degree of similarity is proposed.
For example, in Patent Literature 1, a descriptor is generated from the characteristics of an input image, and the descriptor is recorded using a descriptor database that records the descriptor and indicates a list of images including the generated characteristics. A technique for matching an image with an image in a database is disclosed. The descriptor is chosen to be invariant to distortion caused by digitization of the image and differences between the input image and the matching image in the database.
In this technique, when the descriptor database is scanned, votes for each image in the database are accumulated, and an image that matches the input image with one document with the highest number of votes or an image that has exceeded a certain threshold is obtained. Are extracted as being similar to.
In Patent Document 2, a plurality of feature points are extracted from a digital image, a set of local feature points is determined for each extracted feature point, and a subset of feature points is selected from each determined set Then, as an amount to characterize each selected subset, invariants for geometric transformation are obtained based on a plurality of combinations of feature points in the subset, and feature amounts are calculated by combining the obtained invariants. A technique for searching for an image corresponding to the digital image by voting on an image in a database having the calculated feature amount is disclosed.
Japanese Patent Laid-Open No. 7-282088 (released on October 27, 1995) International Publication No. 2006/092957 pamphlet (published on September 8, 2006) Tomohiro Nakai, Koichi Kise, Masakazu Iwamura: "Document Image Retrieval and Projection Distortion Correction Based on Double Ratio Voting", Image Recognition and Understanding Symposium (MIRU2005) Pp. 538-545
However, in the conventional image collating apparatus, the input input document is a Nin1 (N = 2, 4, 6, 8, 9, etc.) document in which a plurality of document images are assigned to one document. Even if there is, it is not determined, but is determined in the same manner as a normal document.
Therefore, for example, when an image collation device is mounted on the image data output processing device and the output processing of the image data of the input document is controlled based on the determination result, if the input document is an allocated document, the layout is assigned. Appropriate output processing could not be performed on each of the individual original images.
To give a specific example, as shown in FIG. 24, when A of two original images A and B of a 2-in-1 original is a registered original image, the conventional image matching apparatus is a 2-in-1 original. It cannot be determined, and it is determined only that the input document image is similar to the registered document image. For this reason, if, for example, “prohibit output processing” is set for a registered original image determined to be similar to original image A, output processing similar to original image A is also prohibited for original image B. The user has a problem that even the original image B cannot be copied.
The distribution of the number of inversions (or the number of edges) at which the pixel value changes from 0 to 1 and 1 to 0 for each line in the main scanning direction and the sub-scanning direction of the input document image is obtained from the image data of the input document. For example, it is possible to determine whether or not the input document is an allocated document. However, this method requires a completely different function from the image matching process.
The present invention has been made in view of the above problems, and an object of the present invention is to provide an image matching method, an image matching apparatus, and an image data output capable of determining in an image matching process that an input document is an assigned document. To provide a processing device, a program, and a storage medium.
In order to solve the above-described problem, the image matching apparatus of the present invention is calculated by a feature point calculation unit that calculates a feature point of an input document image from the input image data of the input document and the feature point calculation unit. A feature amount calculation unit that calculates a feature amount of the input document image based on a relative position between the feature points, a feature amount of the input document image calculated by the feature amount calculation unit, and a feature of the registered document image In the image collating apparatus including a similarity determination unit that compares the amount and determines whether or not the input document image is similar to the registered document image, the similarity determination unit is similar Is determined, the image position similar to the registered document image on the input document image based on the coordinate positions of the feature point of the input document image and the feature point of the registered document image having the same feature amount And identify the position of the image Using broadcast, the input document image is characterized by comprising determining document determination section whether or not the N-up document.
According to this, the document determination unit is based on the coordinate position of the feature point having the same feature amount between the input document image determined to be similar by the similarity determination unit and the registered document image. An image position similar to the registered original image on the input original image is specified, and whether or not the input original image is that of the assigned original using the position information, that is, the input original is an assigned original. It is determined whether or not there is.
In the case of an allocated document to which a plurality of document images are allocated, the position of each allocated document image is determined by the allocation condition. Therefore, the positional relationship between the feature point of the input document image and the feature point of the registered document image is obtained based on the coordinate position of the feature point of the input document image and the feature point of the registered document image that have the same feature amount, and the input An image position similar to the registered original image on the coordinates of the original image is specified, and whether or not the input original image is that of the assigned original is determined by whether or not it matches an image position determined in advance by the allocation condition. Can be determined.
That is, according to this, by using the correlation between the feature points of the input document image determined to match the registered document image and the feature points of the corresponding registered document image, the image matching processing function is used to perform input. It can be determined whether or not the document is an allocated document.
The image data of the input manuscript is, for example, image data obtained by reading a manuscript with a scanner, or electronic data created by inputting necessary items using a computer (software) in the format of electronic data. It is. That is, for example, an image printed or described on paper and digitized, and an image directly created as electronic data (such as an electronic application form).
In the image collating apparatus according to the present invention, when the document determination unit determines that the similarity determination unit determines that they are similar, the feature point of the input document image having the same feature amount and the registered document image Based on the coordinate position of each feature point, a coefficient calculation unit that calculates a coefficient representing the positional relationship between the feature point of the input document image and the feature point of the registered document image, and the coefficient calculation unit When the coordinates of the reference point of the registered document image are converted to the coordinates of the input document image using a coefficient, and the converted reference point value satisfies a predetermined condition, the input document image is It can also be set as the structure provided with the allocation determination part determined to be a thing.
According to this, the coefficient calculation unit is based on the coordinate positions of the feature points having the same feature amount between the input document image determined to be similar by the similarity determination unit and the registered document image. The coefficient representing the positional relationship between the feature point of the input document image and the feature point of the registered document image is calculated, and the assignment determination unit uses the calculated coefficient to calculate the coordinates of the reference point of the registered document image. When the converted reference point value satisfies a predetermined condition, it is determined that the input document image is that of the allocated document. The reference points of the registered document image can be, for example, the four corners of the registered document image.
In specifying the image position similar to the registered original image on the coordinates of the input original image, the reference point of the registered original image is used, and the coordinates of the reference point are converted into the coordinates of the input original image. An image position similar to the registered document image on the coordinates can be easily and quickly specified.
In the image collating device of the present invention, when the document determination unit determines that the similarity determination unit is similar, the feature point of the input document image and the feature of the registered document image having the same feature amount match. A coefficient calculation unit that calculates a coefficient representing a positional relationship between the feature point of the input document image and the feature point of the registered document image based on the coordinate position of each point; and the coefficient calculated by the coefficient calculation unit Is used to convert the coordinates of the reference point of the registered document image to the coordinates of the input document image, the converted reference point value satisfies a predetermined condition, and the reference point in the registered document image And the size of the image area of a portion similar to the registered document image in the input document image, obtained from the value of the reference point converted to the coordinates on the input document image. Compared When satisfying the fruit reaches a predetermined condition, it may be configured in which the input document image and a and determines allocation judgment unit is of the N-up document.
In the case of an allocated document, the size of each document image as well as the position of each allocated document image is determined by the allocation conditions. Accordingly, as described above, in addition to the value after the coordinate conversion of the reference point of the registered document image, the size of the image region similar to the registered document image on the input document image (the main scanning direction and sub-scanning of the image region). The determination accuracy can be improved by determining whether or not the document is a layout document in consideration of the ratio of the lengths in the direction).
In order to solve the above problems, an image data output processing apparatus of the present invention is an image data output processing apparatus that performs output processing on image data of an input document that has been input, and includes the image collating apparatus of the present invention. And an output processing control unit that controls output processing for the image data of the input document based on the determination result of the image collating device, and the output processing control unit is configured such that when the input document image is that of an allocated document, It is characterized in that control is performed in accordance with each assigned document image.
As already described as the image collating apparatus, in the image collating apparatus according to the present invention, it is possible to determine whether or not the input original image is that of the assigned original using the function of the image collating process. Therefore, in the image data output processing apparatus of the present invention equipped with such an image collation process, when the input document image is that of the allocated document, the output processing control unit responds to each allocated document image. By adopting the control configuration, it is possible to perform an appropriate output process for each assigned original image even when the input original image is that of the assigned original.
In order to solve the above-described problem, the image matching method of the present invention is calculated by a feature point calculating step of calculating a feature point of the input document image from the input image data of the input document and the feature point calculating step. A feature amount calculating step for calculating a feature amount of the input document image based on a relative position between the feature points, a feature amount of the input document image calculated in the feature amount calculation step, and a feature of the registered document image A similarity determination step for determining whether or not the input document image is similar to the registered document image by comparing the input document image with the amount, and similar in the similarity determination step When the determination is made, the position of the registered document image on the input document image is determined based on the coordinate positions of the feature points of the input document image and the feature points of the registered document image having the same feature amount. Constant and, using the information of the position, the input document image is characterized in that it comprises a document determination step of determining whether or not the N-up document.
As already described as the image collating apparatus, according to the above configuration, it is possible to determine whether or not the input document image is that of the allocated document by using the function of image collation processing.
Further, the image collating apparatus may be realized by a computer. In this case, by causing the computer to operate as the respective units, a program for realizing the image collating apparatus by the computer, and a computer reading that records the program. Possible recording media are also included in the scope of the present invention.
The present invention provides an image collating method, an image collating apparatus, an image data output processing apparatus, a program, and a storage medium that can determine in an image collating process that an input original is an allocated original. .
Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a configuration of an image collating apparatus 101 in the present embodiment. The image collating apparatus 101 is provided in, for example, a digital color copying machine (image data output processing apparatus) 102 shown in FIG.
The document to be processed by the image matching apparatus 101 is not particularly specified, but the image matching apparatus 101 has a function of determining similarity between images, and can register an image in advance. Thus, the similarity between the registered image and the image of the document input to be processed can be determined.
Hereinafter, the registered document image is referred to as a registered document image, and the image source is referred to as a registered document. In addition, image data is input so that output processing such as copying, facsimile, or filing can be performed by the digital color copying machine 102, and an original document image that is subjected to determination processing from a registered original image by the image collating apparatus 101 is input document. This is called an image, and the image source is called an input document.
The image collating apparatus 101 determines the similarity between a registered document image and an input document image input to be processed, and outputs a control signal and a document determination signal.
As shown in FIG. 1, the image collation apparatus 101 includes a control unit 1, a document collation processing unit 2, and a memory (storage unit) 3.
The document matching processing unit 2 calculates the feature points of the input document image from the input image data of the input document, calculates the feature amount of the input document image based on the calculated relative position between the feature points, Compared with the registered feature value of the registered document image, the similarity between the input document image and the registered image is determined, and the above-described control signal and document determination signal are output.
In the present embodiment, the document collation processing unit 2 is also provided with a function of registering a document image. During the registration process, the input document image data is registered as a registered document image.
Specifically, the document collation processing unit 2 includes a feature point calculation unit 11, a feature amount calculation unit 12, a voting processing unit 13, a similarity determination processing unit (similarity determination unit) 14, a registration processing unit 15, and a document determination process. Section (original determination section) 16.
When image data of an input document or a registered document is input, the feature point calculation unit 11 extracts a character string or ruled line connection portion from the input image data, and calculates the centroid of the connection portion as a feature point. In the present embodiment, the feature point calculation unit 11 also calculates the coordinates of each feature point.
The feature amount calculation unit 12 uses the feature points calculated by the feature point calculation unit 11, an amount that is invariable with respect to rotation, enlargement, and reduction, that is, rotation of a document image (input document image, registered document image), A feature amount (hash value), which is a parameter that is invariant to a geometric change including translation and enlargement / reduction, is calculated. In order to calculate the feature amount, a feature point near the target feature point is selected and used.
The voting processing unit 13 is registered in a hash table, which will be described later, using the hash value calculated by the feature amount calculation unit 12 for each feature point calculated by the feature point calculation unit 11 from the image data of the input document during the matching process. Vote for registered document images. The voting processing unit 13 votes for a registered document image having the same hash value as the hash value of the image data of the input document. As will be described in detail later, the voting processing unit 13 stores which feature point of the input document image voted to which feature point of which registered document image during the voting process.
The similarity determination processing unit 14 determines whether or not the input document image is similar to the registered document image from the voting process result of the voting processing unit 13. The similarity determination processing unit 14 outputs a control signal corresponding to the determination result according to the determination result.
The registration processing unit 15 identifies a registered document image according to the hash value calculated by the feature amount calculation unit 12 for each feature point calculated by the feature point calculation unit 11 from the image data of the registered document during the registration process. An ID that is index information is registered.
In the document collation processing unit 2, the voting processing unit 13 and the similarity determination processing unit 14 perform processing during the collation processing, and do not perform processing during the registration processing. Conversely, the registration processing unit 15 performs processing during registration processing and does not perform processing during collation processing.
When the similarity determination processing unit 14 determines that the input document image is similar to the registered document image, the document determination processing unit 16 determines each of the feature points of the input document image and the feature points of the registered document image having the same feature amount. The position of the registered document image on the input document image is specified based on the coordinate position of the input document, and it is determined whether or not the input document image is that of the allocated document using the position information. . The document determination processing unit 16 outputs a document determination signal indicating whether the image is an allocated document image according to the determination result.
A control unit (CPU) 1 controls access to the above-described units and the memory 3 in the image collating apparatus 101. The memory 3 is a working memory when the above-described units in the image collating apparatus 101 perform processing, and a place where various information such as an ID representing a registered document image is registered in the registration process. It is.
Hereinafter, the document matching processing unit 2 in the image matching device 101 will be described in detail with reference to the drawings. As shown in FIG. 3, the feature point calculation unit 11 in the document matching processing unit 2 includes an achromatic processing unit 21, a resolution conversion unit 22, an MTF processing unit 23, a binarization processing unit 24, and a centroid calculation unit 25. ing. FIG. 3 is a block diagram showing a configuration of the feature point calculation unit 11.
When the input image data, which is image data such as a registered document or input document, is a color image, the achromatic processing unit 21 achromatizes the input image data and converts it into a brightness or luminance signal. For example, the luminance Y is obtained from the following equation.
The achromatization process is not limited to the above method, and the RGB signal is converted into a CIE 1976 L * a * b * signal (CIE: Commission International de l'Eclairage, L * : brightness index, a * , b * : color. (Degree index).
When the input image data is optically scaled by the image input device, the resolution conversion unit 22 scales the input image data again so as to obtain a predetermined resolution. The image input device is, for example, a scanner that reads an image of a document and converts it into image data, and the color image input device 111 corresponds to that in the digital color copying machine 102 shown in FIG.
Further, the resolution conversion unit 22 is also used as a resolution conversion unit for lowering the resolution from the resolution read by the image input device with the same magnification setting in order to reduce the data processing amount in the subsequent stage. For example, image data read at 600 dpi (dot per inch) is converted to 300 dpi.
The MTF processing unit 23 is used to absorb the influence caused by the difference in the spatial frequency characteristics of the image input device for each type of image input device. In other words, the image signal output from the CCD provided in the image input device includes optical components such as lenses and mirrors, aperture aperture of the light receiving surface of the CCD, transfer efficiency and afterimage, integration effects due to physical scanning, and uneven operation. This causes degradation of MTF. Due to the deterioration of the MTF, the read image is blurred. Therefore, the MTF processing unit 23 performs a process of repairing the blur caused by the degradation of the MTF by performing an appropriate filter process (enhancement process). Further, it is also used to suppress high-frequency components that are not necessary for processing in the feature point extraction unit 31 of the subsequent feature amount calculation unit 12. That is, the enhancement and smoothing processing is performed using the mixing filter. In addition, the filter coefficient of this mixing filter is shown in FIG. 4, for example.
The binarization processing unit 24 binarizes the image data by comparing the brightness value (brightness signal) or brightness value (brightness signal) of the image data achromatized by the achromatization processing unit 21 with a threshold value. The binarized image data (registered document image, binary image data of the input document image) is stored in the memory 3.
The center-of-gravity calculation unit 25 performs labeling (labeling processing) on each pixel of the image data binarized by the binarization processing unit 24 (for example, image data represented by “1” and “0”). Do. In this labeling, the same label is given to the pixels showing the same value among the two values. Next, a connected area, which is an area composed of a plurality of pixels formed by connecting pixels with the same label, is specified. Next, the center of gravity of the identified connected area is extracted as a feature point, and the extracted feature point is output to the feature amount calculation unit 12. Here, the feature point can be represented by coordinate values (x coordinate, y coordinate) in the binary image, and the coordinate value of the feature point is also calculated and output to the feature amount calculation unit 12.
FIG. 5 is an explanatory diagram showing an example of a connected area extracted from binarized image data and the center of gravity of this connected area. The connected area corresponding to the letter “A” and the center of gravity of the connected area (features) Point). FIG. 6 is an explanatory diagram illustrating an example of each centroid (feature point) of a plurality of connected regions extracted from a character string included in binarized image data.
As shown in FIG. 7, the feature amount calculation unit 12 includes a feature point extraction unit 31, an invariant calculation unit 32, and a hash value calculation unit 33. FIG. 7 is a block diagram showing a configuration of the feature amount calculation unit 12.
When there are a plurality of feature points calculated by the feature point calculation unit 11 in the image data, the feature point extraction unit 31 sets one feature point as the feature point of interest, and sets feature points around the feature point of interest as A predetermined number of peripheral feature points are extracted in order from the closest distance from the feature point of interest. In the example of FIG. 8, when the predetermined number is 4 points and the feature point a is the feature point of interest, the feature points b, c, d, e are extracted as the peripheral feature points, and the feature point b is noted. In the case of feature points, four feature points a, c, e, and f are extracted as peripheral feature points.
The feature point extraction unit 31 also extracts a combination of three points that can be selected from the four peripheral feature points extracted as described above. For example, as shown in FIGS. 9A to 9D, when the feature point a shown in FIG. 8 is the feature point of interest, three of the peripheral feature points b, c, d, and e are selected. Combinations, that is, combinations of peripheral feature points b, c, d, peripheral feature points b, c, e, peripheral feature points b, d, e, and peripheral feature points c, d, e are extracted.
The invariant calculation unit 32 calculates, for each combination extracted by the feature point extraction unit 31, an invariant (one of feature amounts) Hij with respect to geometric deformation.
Here, i is a number indicating the feature point of interest (i is an integer equal to or greater than 1), and j is a number indicating a combination of three peripheral feature points (j is an integer equal to or greater than 1). In the present embodiment, the ratio of two of the lengths of the line segments connecting the peripheral feature points is set as the invariant Hij.
The length of the line segment can be calculated based on the coordinate value of each peripheral feature point. For example, in the example of FIG. 9A, if the length of the line segment connecting the feature point b and the feature point c is A11 and the length of the line segment connecting the feature point b and the feature point d is B11, The variable H11 is H11 = A11 / B11. In the example of FIG. 9B, if the length of the line segment connecting the feature point b and the feature point c is A12 and the length of the line segment connecting the feature point b and the feature point e is B12, The variable H12 is H12 = A12 / B12. In the example of FIG. 9C, if the length of the line segment connecting the feature point b and the feature point d is A13 and the length of the line segment connecting the feature point b and the feature point e is B13, The variable H13 is H13 = A13 / B13. In the example of FIG. 9D, if the length of the line segment connecting the feature point c and the feature point d is A14 and the length of the line segment connecting the feature point c and the feature point e is B14, The variable H14 is H14 = A14 / B14. In this way, invariants H11, H12, H13, and H14 are calculated in the examples of FIGS. 9A to 9D.
In the above example, the line segment connecting the peripheral feature point closest to the target feature point and the second closest peripheral feature point is Aij, and the peripheral feature point closest to the target feature point and the third closest peripheral feature point are The line segment connecting the two is defined as Bij. However, the present invention is not limited to this, and the line segment used for calculating the invariant Hij may be selected by an arbitrary method.
The hash value calculation unit 33 is, for example,
Hi = (Hi1 × 10 3 + Hi2 × 10 2 + Hi3 × 10 1 + Hi4 × 10 0 ) / D
Is calculated as a hash value (one of feature quantities) Hi and stored in the obtained hash value memory 8. Note that D is a constant set in advance according to how much the range of values that the remainder can take is set.
The method for calculating the invariant Hij is not particularly limited. For example, a multi-ratio of five points near the feature point of interest, a multi-ratio of five points extracted from n points in the vicinity (n is an integer of n ≧ 5), m points extracted from the n points in the vicinity (m is m <n and A value calculated based on the arrangement of m ≧ 5) and a cross ratio of five points extracted from m points may be used as the invariant Hij for the feature point of interest. The cross ratio is a value obtained from four points on a straight line or five points on a plane, and is known as an invariant with respect to projective deformation which is a kind of geometric transformation.
Further, the formula for calculating the hash value Hi is not limited to the above formula, and other hash functions (for example, any one of the hash functions described in Patent Document 2) may be used.
When the extraction of the peripheral feature points for one target feature point and the calculation of the hash value Hi are finished, each unit of the feature amount calculation unit 12 changes the target feature point to another feature point and extracts the peripheral feature points and the hash value. To calculate hash values for all feature points.
In the example of FIG. 8, when the extraction of the peripheral feature points and the hash value when the feature point a is the target feature point is finished, the peripheral feature points and the hash value are extracted when the feature point b is the target feature point. I do. In the example of FIG. 8, when the feature point b is the target feature point, four feature points a, c, e, and f are extracted as the peripheral feature points.
Then, as shown in FIGS. 10A to 10D, a combination of three points selected from these peripheral feature points a, c, e, and f (peripheral feature points a, e, f, and peripheral points) Feature points a, e, c, peripheral feature points a, f, c, and peripheral feature points e, f, c) are extracted, and a hash value Hi is calculated for each combination and stored in the memory 3. Then, this process is repeated for each feature point, and a hash value when each feature point is a feature point of interest is obtained and stored in the memory 3.
In addition, when performing the registration process, the feature amount calculation unit 12 stores the hash value (feature amount) for each feature point of the input image data (image data of the registered document) calculated as described above. Send to.
The registration processing unit 15 stores a hash value for each feature point calculated by the feature amount calculation unit 12 and an ID for identifying a registered document image of the input image data in a hash table (not shown) provided in the memory 3. Registration is performed sequentially (see FIG. 11A). If a hash value has already been registered, an ID is registered in association with the hash value. IDs are sequentially assigned numbers without duplication.
When the number of registered document images registered in the hash table exceeds a predetermined value (for example, 80% of the number of document images that can be registered), old IDs may be searched and sequentially deleted. . The erased ID may be used again as the ID of a new registered document image. Further, when the calculated hash values are the same value (H1 = H5 in the example of FIG. 11B), these may be combined and registered in the hash table.
Further, when performing the collation processing, the feature amount calculation unit 12 sends a hash value for each feature point of the input image data (input original image data) calculated as described above to the voting processing unit 13.
The voting processing unit 13 compares the hash value of each feature point calculated from the input image data with the hash value registered in the hash table, and votes for the registered document image having the same hash value (see FIG. 12). FIG. 12 is a graph showing an example of the number of votes for three registered document images ID1, ID2, and ID3. In other words, the voting processing unit 13 counts the number of times that the same hash value as the hash value of the registered document image is calculated from the input image data for each registered document image, and stores the count value in the memory 3.
Further, in the example of FIG. 11B, H1 = H5, and these are grouped into one of H1 and registered in the hash table. In such a table value, the input image calculated from the input image data is used. If H1 is included in the hash value of the registered document image ID1, two votes are cast.
At this time, the voting processing unit 13 uses the feature points of the input document image and the feature points of the registered document image having the same hash value to obtain the positional relationship between the feature points. That is, the feature points of the input document image and the feature points of the registered document image are aligned. Then, as shown in FIG. 13, which feature point of the input document image has voted for which feature point of which registered document image. Here, p (p1, p2, p3,...) Is index information representing each feature point of the input document image, and f (f1, f2, f3,...) Represents each feature point of the registered document image. Index information.
Further, as shown in FIG. 14, f representing each feature point of the registered document image and coordinates on each registered document image are stored in advance, and collation determination is performed including the coordinate position.
In the example of FIG. 13, the feature value (hash value) obtained for the feature point p1 of the input document image matches the feature value of the feature point f1 of the registered document image ID1, and the feature point p2 of the input document image It is determined that the feature amount (hash value) calculated for the feature point coincides with the feature amount of the feature point f2 of the registered document image ID3 (the above content is described in Non-Patent Document 1).
The similarity determination processing unit 14 extracts the ID of the registered document image and the number of votes obtained from the voting process result of the voting processing unit 13 and obtains the extracted number of votes as a predetermined threshold value. The degree of similarity is calculated by comparison, or the extracted number of votes is normalized by dividing by the maximum number of votes the document has, and the result is compared with a predetermined threshold value. As an example of the threshold value in this case, for example, a method of setting to 0.8 or more can be mentioned. If there is a handwritten part, the number of votes may be greater than the maximum number of votes, so the similarity may be greater than one.
The maximum number of votes is represented by the number of feature points × the number of hash values calculated from one feature point (attention feature point). In the examples of FIGS. 9 and 10 described above, an example in which one hash value is calculated from one feature point is shown as the simplest example, but the method of selecting feature points around the target feature point is changed. A plurality of hash values can be calculated from one feature point. For example, there are six combinations of extracting six points as feature points around the feature point of interest and extracting five points from these six points. Then, for each of these six ways, there is a method of calculating a hash value by extracting three points from five points to obtain an invariant.
The similarity determination processing unit 14 outputs a control signal according to the determination result. The control signal is for controlling output processing performed on the image data of the input document by the digital color copying machine 102. When it is determined that the input document image is similar to the registered document image, the image collating apparatus 101 according to the present embodiment outputs a control signal according to the restriction set for the registered document image, and the image data of the input document Perform the output process for. In the case of the color copying machine 102, copying is prohibited or copying with the image quality is forcibly reduced. If they are not similar, a control signal “0” is output.
As described above, when the similarity determination processing unit 14 determines that the input document image is similar to the registered document image, the document determination processing unit 16 and the feature points of the input document image having the same feature amount and the registered document image The position of the registered document image on the input document image is specified based on the coordinate position of each of the feature points, and using the position information, it is determined whether or not the input document image is that of the allocated document. Judgment.
In the present embodiment, when the document determination processing unit 16 determines that the similarity is determined to be similar by the similarity determination processing unit 14, the feature point of the input document image and the feature point of the registered document image that have the same feature amount match. Based on each coordinate position, a coefficient calculating unit that calculates a coefficient representing a positional relationship between the feature point of the input document image and the feature point of the registered document image, and an assignment determination unit to be described later are provided.
The coefficient calculation unit obtains a coefficient indicating the positional relationship between the feature point of the input document image obtained by the voting processing unit 13 and the coordinate position of the feature point of the registered document image. Here, how to obtain the coefficient will be described.
The coefficient calculation unit converts the coordinate system of the read input document image into the coordinate system of the registered document image and performs alignment in order to grasp the correspondence between the feature point of the input document image and the feature point of the registered document image. Do. Specifically, first, based on the results of FIGS. 13 and 14, the coordinates of the feature points of the registered document image having the same feature value (hash value) and the coordinates of the feature points of the read input document image. Take correspondence with.
FIG. 15 is an explanatory diagram of an operation for aligning the registered document image and the input document image based on the feature points of the registered document image and the feature points of the input document image having the same feature amount (hash value). . FIG. 16 is an explanatory diagram illustrating a correspondence relationship between the coordinates of the feature points of the registered document image and the coordinates of the feature points of the input document image obtained as a result of alignment between the registered document image and the input document image. 15 and 16 show a case where there are four feature points having the same feature amount (hash value) between the registered document image and the input document image.
Next, the coefficient calculation unit sets Pin as the matrix for the feature point coordinates of the registered document image, Pout as the matrix for the feature point coordinates of the input document image, and A as the conversion coefficient. A is calculated.
Here, since Pin is not a square matrix, both sides are multiplied by a Pin transposed matrix Pin T , and further, an inverse matrix of Pin T Pin is multiplied.
Next, the coordinate position of the input document image is calculated using the conversion coefficient A thus obtained. In this case, as shown in the following expression, arbitrary coordinates (x, y) on the registered original image are converted into coordinates (x ′, y ′) on the input original image using the conversion coefficient A.
The assignment determination unit converts the coordinates of the reference point of the registered document image into the coordinates of the input document image using the conversion coefficient A calculated by the coefficient calculation unit, and the value of the converted reference point is determined in advance. When the condition is satisfied, it is determined that the input document image is that of the allocated document.
The assignment determination unit uses the conversion coefficient A to convert the coordinates of the four corners of the registered document image into the coordinates of the input document image, and performs threshold processing on the converted coordinate position to determine whether the document is an assigned document. The document determination signal indicating whether the document is an allocated document is output. In the case of an allocated document, information indicating the image position of a portion similar to the registered document image in the input document image is also output.
Here, a process for determining whether or not the document is an assigned document by threshold processing will be described with a specific example. The registered document is A4 size (210 mm × 297 mm), the effective image area is 190 mm × 257 mm, and the resolution is 600 dpi (pixel number: 4488 × 6070). The size of the registered document image, which is the size on the image data obtained by reading the registered document, is the same as the size of the registered document.
1) As shown in FIG. 17, the coordinates of the four corners of the registered document image are (a1, b1), (a2, b1), (a1, b2), (a2, b2), and the four corner coordinates after conversion. The document determination processing unit 16 converts the (coordinates on the input document) as (A1 ′, B1 ′), (A1 ′, B2 ′), (A2 ′, B1 ′), (A2 ′, B2 ′). Later coordinates are
−224 ≦ A1 ′ ≦ 224, 3205 ≦ B1 ′ ≦ 3811,
4736 ≦ A2 ′ ≦ 5184, −303 ≦ B2 ′ ≦ 303,
Is satisfied, it is determined that the input document image is of a 2-in-1 document. Note that the position of an image similar to the registered document image in the input document image is specified by the coordinates of the four corners after conversion.
The above value is determined based on the size of the document image (size of the document). That is, when the effective image area is 190 mm × 257 mm (number of pixels 4488 × 6070 (600 dpi)), the number of pixels of the entire document image is 4960 × 7016. Accordingly, when (A1 ′, B2 ′) = origin (0, 0) at the upper left of the original image, (A1 ′, B1 ′) − = (0, 7016/2), (A2 ′, B1 ′) = (4960, 7016/2), (A2 ′, B2 ′) = (4960, 0). The coordinate variation width is given a width of ± 5% of the number of pixels in the main scanning direction and the sub-scanning direction of the effective image area.
Here, the lower limit of A1 ′ is −224, and the lower limit of B2 ′ is −303, as shown in FIGS. 18A to 18D, the registered original image is set to the coordinates of the input original image. This is because there is a case where the original document image is shifted beyond the origin (0, 0). Further, the value of the fluctuation range may be set so that it can be appropriately determined whether or not the document is a 2-in-1 document.
If the accuracy of determination is further increased, not only the coordinates of the four corners after the conversion as described above but also the following formula may be used to further consider the size ratio of the document image.
2) Also, as shown in FIG. 19, the coordinates of the four corners of the registered document image are (a1, b1), (a2, b1), (a1, b2), (a2, b2), and the four corners after conversion. Are (A1 ″, B1 ″), (A2 ″, B1 ″), (A1 ″, B2 ″), (A2 ″, B2 ″) as the coordinates of the original document image processing unit 16. Is the transformed coordinates,
−112 ≦ A1 ″ ≦ 112, −151 ≦ B1 ”≦ 151,
2368 ≦ A2 ″ ≦ 2592, 3357 ≦ B2 ″ ≦ 3659
Is satisfied, it is determined that the input document image is a 4-in-1 document.
When (A1 ″, B1 ″) = origin (0, 0) in the upper left of the original image, (A1 ″, B2 ″) − = (0, 7016/2), (A2 ″, B2 ″) = (4960 / 2, 7016/2), (A2 ″, B1 ″) = (4960/2, 0). The coordinate variation width is given as ± 2.5% of the number of pixels in the main scanning direction and the sub-scanning direction of the effective image area. Also in this case, the value of the fluctuation range may be set so that it can be appropriately determined whether or not the document is a 4-in-1 document.
In order to further increase the determination accuracy, the ratio of the size of the document image area may be taken into consideration using the following formula, as in the case of a 2-in-1 document.
In the case of the digital color copying machine (image data output processing device) 102, the control signal and the document determination signal are input to the editing processing unit 126 in the color image processing device 112 shown in FIG.
If the input document image is that of the assigned document and the assigned document image is similar to the registered document image based on the control signal and the document determination signal, the edit processing unit 126 inputs the input document. Only the image of the area similar to the registered original image in the image is output according to the control signal according to the restriction set for the registered original image (copy prohibited, original image filling or blank page is output (data value is “0” or Etc.) and other image areas are output as they are.
As a result, as shown in FIGS. 20A and 20B, even when the input document image is a 2-in-1 document image or 4-in-1 document image including the registered document image A for which output processing is prohibited, the registered document image A The restriction set only for the portion is applied, and other B, C, and D document images included in the input document image can be output without problems.
Next, the configuration of the digital color copying machine 102 provided with the image collating apparatus 101 will be described. FIG. 2 is a block diagram showing the configuration of the digital color copying machine 102.
As shown in FIG. 2, the digital color copying machine 102 includes a color image input device 111, a color image processing device 112, a color image output device 113, and an operation panel 114.
The color image input device 111 includes a scanner unit including a device that converts optical information such as a CCD (Charge Coupled Device) into an electrical signal, and outputs a reflected light image from the original as an RGB analog signal.
An analog signal read by the color image input device 111 is converted into an A / D conversion unit 121, a shading correction unit 122, a document type automatic discrimination unit 123, a document matching processing unit 124, an input gradation in the color image processing device 112. The order of the correction unit 125, the editing processing unit 126, the region separation processing unit 127, the color correction unit 128, the black generation and lower color removal unit 129, the spatial filter processing unit 130, the output gradation correction unit 131, and the gradation reproduction processing unit 132. And output to the color image output device 113 as a CMYK digital color signal.
The A / D conversion unit 121 converts RGB analog signals into digital signals. The shading correction unit 122 applies a color image input device to the digital RGB signals sent from the A / D conversion unit 121. Processing for removing various distortions generated in the illumination system 111, imaging system, and imaging system 111 is performed. At the same time as adjusting the color balance, the RGB reflectance signal is converted into a signal that can be easily handled by the image processing system employed in the color image processing apparatus 112, such as a density signal.
The automatic document type discrimination unit 123 determines whether the read document is a text document or a printed photograph based on the RGB signal (RGB density signal) in which various distortions have been removed by the shading correction unit 122 and the color balance has been adjusted. Whether the document is a document or a character-printed photo document in which characters and a printed photo are mixed is determined.
The document matching processing unit 124 determines the similarity between the image data (input document image) of the input document input and the registered document image registered in advance, and outputs a control signal according to the result. Here, it is also determined whether the document is an allocated document, and a document determination signal is also output. That is, it corresponds to the document matching processing unit 2 of the image matching apparatus 101 in FIG. When the input original image is that of the assigned original and some of the assigned images are similar to the registered original image, the document collation processing unit 124 outputs the portion of the input original image when the output image data is output. Copying is prohibited only for images. Further, the document collation processing unit 124 outputs the RGB data of the input image data as it is to the input tone correction unit 125 at the subsequent stage.
In the input tone correction unit 125, image quality adjustment processing such as background density removal and contrast is performed on the RGB signal from which various distortions are removed by the shading correction unit 122.
In the editing processing unit 126, when the input original image is an assigned original image and an original image similar to the registered original image is assigned, only the image portion is not copied (copy prohibited, original image filling). Or a blank page (replace the data value with “0” or “255 (in case of 8 bits)”). When the processing for the allocated document is not performed, the processing of the editing processing unit is through (the processing is not performed).
The region separation processing unit 127 separates each pixel in the input image into one of a character region, a halftone dot region, and a photograph region from the RGB signal. Based on the separation result, the region separation processing unit 127 outputs a region identification signal indicating which region the pixel belongs to to the black generation and under color removal unit 129, the spatial filter processing unit 130, and the gradation reproduction processing unit 132. In addition, the input signal output from the editing processing unit 126 is output to the subsequent color correction unit 128 as it is.
The color correction unit 128 performs a process of removing color turbidity based on the spectral characteristics of the CMY color material including unnecessary absorption components in order to make color reproduction faithful.
The black generation and under color removal unit 129 generates black (K) signals from the CMY three-color signals after color correction, and subtracts the K signals obtained by black generation from the original CMY signals to generate new CMY signals. The process to generate is performed. As a result, the CMY three-color signal is converted into a CMYK four-color signal.
The spatial filter processing unit 130 performs spatial filter processing using a digital filter on the image data of the CMYK signal input from the black generation and under color removal unit 127 to correct the spatial frequency characteristics. As a result, blurring of the output image and deterioration of graininess can be reduced.
Similar to the spatial filter processing unit 130, the gradation reproduction processing unit 132 performs predetermined processing described later on the image data of the CMYK signal based on the region identification signal.
For example, in the region separated into characters by the region separation processing unit 127, a filter with a high enhancement amount of high-frequency components is used for the spatial filter in the spatial filter processing unit 130 in order to improve the reproducibility of characters. At the same time, the tone reproduction processing unit 132 performs binarization or multi-value processing using a high-resolution screen suitable for reproducing high-frequency components.
Further, with respect to the region separated into halftone dots by the region separation processing unit 127, the spatial filter processing unit 130 performs low-pass filter processing for removing the input halftone component. The output tone correction unit 131 performs output tone correction processing for converting a signal such as a density signal into a halftone dot area ratio which is a characteristic value of the color image output device 113, and then the tone reproduction processing unit 132. A gradation reproduction process is performed in which the image is finally separated into pixels and each gradation is reproduced. With respect to the region separated into photographs by the region separation processing unit 127, binarization or multi-value processing is performed on the screen with an emphasis on gradation reproducibility.
The image data subjected to the above-described processes is temporarily stored in a storage device (not shown), read out at a predetermined timing, and input to the color image output device 113.
The color image output device 113 outputs image data on a recording medium such as paper. For example, a color imageless output device using an electrophotographic method or an ink jet method can be used, but the color image output device 113 is particularly limited. It is not a thing. The above processing is controlled by a CPU (Central Processing Unit) not shown.
In the above configuration, the operation of the image collating apparatus 101 of the present embodiment will be described below based on the flowchart of FIG.
First, the control unit 1 determines whether or not the registration mode is selected (S1). The registration mode is selected by operating the operation panel 114 in the digital color copying machine 102. Further, in an image processing system including the image processing device 112 and a terminal device (computer) connected thereto, the image processing device 112 is selected by an input operation from the terminal device.
When the registration mode is selected, the feature point calculation unit 11 calculates each feature point of the registered document image based on the input image data (S2), and calculates the coordinates of these feature points (S3).
Next, the feature amount calculation unit 12 calculates the feature amount of each feature point calculated by the feature point calculation unit 11 (S4), and the registration processing unit 15 performs the above-described feature points of the document to be registered. The feature amount (hash value) of the feature point, the index f of the feature point, and the coordinates of the feature point are stored in the memory 3, and the operation is terminated (S5). As a result, the table shown in FIG. 14 representing f representing each feature point of the registered document and coordinates on the image of the registered document is obtained.
On the other hand, when the registration mode is not selected in S1, the control unit 1 determines that the verification mode is selected, and proceeds to S11. In S11, the feature point calculation unit 11 calculates each feature point of the input document image based on the input image data, and further calculates the coordinates of these feature points (S12).
Next, the feature amount calculation unit 12 calculates the feature amount of each feature point calculated by the feature point calculation unit 11 (S13), and the voting processing unit 13 uses the calculated feature amount of the object document. A voting process is performed (S14).
Next, the similarity determination processing unit 14 determines whether the input document image is similar to any registered document image based on the result of the voting process (S15). Here, if it is not similar to any registered document image, the similarity determination processing unit 14 outputs a determination signal “0” (S21), and the operation is terminated.
On the other hand, if the image is similar to any registered document image, the similarity determination processing unit 14 selects a feature point having a matching feature amount (S16), and sets a document conversion coefficient A for the input document image of the registered document image. Obtain (S17).
Then, using the obtained conversion coefficient A, the coordinates of the registered original image are converted into the coordinates of the input original image, and it is determined whether or not the input original image is that of the assigned original (S18).
If it is determined in S18 that the document is an allocated document, a control signal for output processing is performed only at a portion similar to the registered document image with the restriction set in the registered document image (S19), and the operation is terminated.
On the other hand, if it is determined in S18 that the document is not an assigned document, a control signal for outputting the entire input document image with the restriction set in the registered document image is output (S20), and the operation is terminated.
As described above, the image matching apparatus 101 according to the present embodiment calculates the feature points of the input document image from the input image data of the input document, and inputs based on the calculated relative positions of the feature points. The feature amount of the document image is obtained, and the obtained feature amount is compared with the feature amount of the registered document image to determine whether or not the input document image is similar to the registered document image. Then, the document determination processing unit 16 specifies the position of the registered document image on the input document image based on the coordinate positions of the feature points of the input document image and the feature points of the registered document image having the same feature amount. Then, using the position information, it is determined whether or not the input document image is that of the allocated document.
Thus, using the correlation between the feature points of the input document image determined to match the registered document image and the feature points of the corresponding registered document image, the input document is assigned to the assigned document. It can be determined whether or not.
FIG. 22 is a block diagram illustrating a configuration of a digital color multifunction peripheral (image data output processing apparatus) 103 provided with the image collating apparatus 101 of the present embodiment.
The digital color multifunction peripheral 103 has a configuration in which a communication device 115 such as a modem or a network card is added to the digital color copying machine 102 shown in FIG.
In this digital color multifunction peripheral 103, when performing facsimile transmission, the communication device 115 performs a transmission procedure with the other party, and when a state where transmission is possible is ensured, image data compressed in a predetermined format (scanner) Is read from the memory 3 and necessary processing such as changing the compression format is performed, and the image data is sequentially transmitted to the other party via the communication line.
When receiving a facsimile, the image data transmitted from the other party is received and input to the color image processing apparatus 116 while performing a communication procedure. In the color image processing apparatus 116, the received image data is expanded by a compression / expansion processing unit (not shown). The decompressed image data is subjected to rotation processing and resolution conversion processing as necessary, and subjected to output tone correction (output tone correction unit 131) and tone reproduction processing (tone reproduction processing unit 132). Are output from the color image output device 113.
The digital color multifunction peripheral 103 performs data communication with a computer or other digital multifunction peripheral connected to the network via a network card or a LAN cable.
In the above example, the digital color multifunction peripheral 103 has been described. However, the multifunction peripheral may be a monochrome multifunction peripheral. A single facsimile communication apparatus may be used.
Further, the image collating apparatus 101 of the present embodiment can also be applied to an image reading apparatus. FIG. 23 is a block diagram showing the configuration of a color image reading apparatus (image data output processing apparatus) 104 to which the image collating apparatus 101 of this embodiment is applied. The color image reading device 104 is, for example, a flat bed scanner, and may be a digital camera.
The color image reading device 104 includes a color image input device 111 and a color image processing device 117. The color image processing device 117 includes an A / D conversion unit 121, a shading correction unit 122, an original type automatic determination unit 123, a document collation. A processing unit 124 is provided. The document matching processing unit 124 corresponds to the document matching processing unit 2 in the image matching device 101 shown in FIG.
The color image input device 111 (image reading means) is composed of, for example, a scanner unit equipped with a CCD (Charge Coupled Device), and the reflected light image from the original is converted into RGB (R: red, G: green, B: blue). The analog signal is read by the CCD and input to the color image processing device 117.
The analog signal read by the color image input device 111 is converted into an A / D (analog / digital) conversion unit 121, a shading correction unit 122, a document type automatic discrimination unit 123, a document matching processing unit in the color image processing device 117. Sent in the order of 124.
The A / D converter 121 converts RGB analog signals into digital signals, and the shading correction unit 122 applies a color image input device to the digital RGB signals sent from the A / D converter 121. Processing for removing various distortions generated in the illumination system 111, imaging system, and imaging system 111 is performed. The shading correction unit 122 adjusts the color balance and converts the RGB reflectance signal into a density signal.
The functions of the document type automatic discrimination unit 123 and the document collation processing unit 124 are as described above. The document matching processing unit 124 determines the similarity between the input original document image and the registered original image, and controls the control signal (copy, electronic distribution, filing prohibition or electronic distribution to a predetermined address in accordance with the result. Or filing to a folder or the like, or filing to a predetermined folder or electronic distribution to a predetermined address may be output. Here, the control signal is transmitted together with the read image data to a printer or a multifunction peripheral via a network and output. Or it inputs into a printer via a computer or directly into a printer. In this case, it is necessary to be able to determine a signal representing the processing contents on the printer, the multifunction peripheral, or the computer side. Instead of outputting the control signal, the calculated feature amount of the input document image may be output, and the server, the computer, or the printer may make a collation determination with the registered document image. A digital camera may be used as the image reading device.
In the above embodiment, a configuration in which the document type automatic determination unit 123 is provided is illustrated, but a configuration in which the document type automatic determination unit 123 is not provided may be employed.
The present invention performs the above-described similarity determination (image collation) and output control on a computer-readable recording medium in which program codes (executable program, intermediate code program, source program) of a program to be executed by a computer are recorded. An image processing method may be recorded. As a result, the recording medium on which the program code for performing the similarity determination unit, the output control, and the image processing method for registering the original image is recorded can be provided in a portable manner.
In this embodiment, since the processing is performed by a microcomputer, the recording medium may be a memory (not shown), for example, a ROM itself as a program medium, or an external storage device (see FIG. (Not shown) may be a program medium provided with a program reading device, which can be read by inserting a recording medium therein.
In any case, the stored program may be configured to be accessed and executed by the microprocessor, or in any case, the program code is read and the read program code is stored in the microcomputer. The program code may be downloaded to a program storage area (not shown) and the program code is executed. It is assumed that this download program is stored in the main device in advance.
Here, the program medium is a recording medium configured to be separable from the main body, such as a tape system such as a magnetic tape or a cassette tape, a magnetic disk such as a floppy (registered trademark) disk or a hard disk, or a CD-ROM / MO /. MD / DVD optical discs, IC cards (including memory cards) / optical cards, etc. Mask ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash A medium that carries a fixed program including a semiconductor memory such as a ROM may be used.
In the present embodiment, since the system configuration is such that a communication network including the Internet can be connected, it may be a medium that fluidly carries a program so as to download a program code from the communication network. When the program code is downloaded from the communication network in this way, the download program may be stored in the main device in advance or may be installed from another recording medium. The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program code is embodied by electronic transmission.
The computer system also displays an image input device such as a flatbed scanner, a film scanner, and a digital camera, a computer that performs various processes such as the above image processing method by loading a predetermined program, and displays the processing results of the computer. It comprises an image display device such as a CRT display / liquid crystal display and a printer that outputs the processing results of the computer to paper or the like. Furthermore, a network card, a modem, and the like are provided as communication means for connecting to a server or the like via a network.
It is a block diagram which shows the structure of the image collation apparatus of embodiment of this invention. FIG. 2 is a block diagram illustrating a configuration of a digital color copying machine that is an image data output processing apparatus including the image collating apparatus illustrated in FIG. 1. It is a block diagram which shows the structure of the feature point calculation part in the image collation apparatus shown in FIG. It is explanatory drawing which shows the filter coefficient of the mixing filter with which the MTF process part in the feature point calculation part shown in FIG. 3 is provided. It is explanatory drawing which shows an example of the connection area | region extracted from the image data binarized by the process of the feature point calculation part shown in FIG. 3, and the gravity center of this connection area | region. It is explanatory drawing which shows an example of each gravity center (feature point) of the some connection area | region extracted from the character string contained in the binarized image data by the process of the feature point calculation part shown in FIG. It is a block diagram which shows the structure of the feature-value calculation part in the image collation apparatus shown in FIG. It is explanatory drawing of the extraction operation | movement of the surrounding feature point with respect to the attention feature point in the feature point extraction part in the feature-value calculation part shown in FIG. FIG. 9A shows an example of a combination of three points that can be selected from the four peripheral feature points extracted by the feature point extraction unit shown in FIG. FIG. 9B is an explanatory diagram illustrating an example of a combination of peripheral feature points b, c, and d. FIG. 9B is an explanatory diagram illustrating an example of a combination of peripheral feature points b, c, and e with respect to the target feature point a. FIG. 9C is an explanatory diagram showing an example of a combination of peripheral feature points b, d, and e with respect to the target feature point a, and FIG. 9D is a combination of peripheral feature points c, d, and e with respect to the target feature point a. It is explanatory drawing which shows the example of. FIG. 10A shows three peripheral feature points that can be selected when the target feature point is transferred to one of the four peripheral feature points extracted by the feature point extraction unit shown in FIG. FIG. 10B is a diagram illustrating an example of the combination, and is an explanatory diagram illustrating an example of the combination of the peripheral feature points a, e, and f with respect to the target feature point b. FIG. FIG. 10C illustrates an example of a combination of peripheral feature points a, f, and c with respect to the feature point of interest b, and FIG. 10D illustrates an example of the combination of e and c. It is explanatory drawing which shows the example of the combination of the surrounding feature points e, f, and c with respect to the attention feature point b. FIGS. 11A and 11B are explanatory diagrams illustrating an example of a hash value and a registered image index for each feature point stored in the memory in the image collating apparatus illustrated in FIG. 1. It is a graph which shows an example of the voting result by the voting process part in the image collation apparatus shown in FIG. FIG. 3 is an explanatory diagram of a table storing correspondences between feature points of an input document image and feature points of a registered document image of a vote destination, which are stored in a memory in the image collating apparatus shown in FIG. 1. FIG. 3 is an explanatory diagram of a table showing the correspondence between the index f and the coordinate value of a feature point of a registered document image stored for each registered document image, which is stored in the memory in the image collating apparatus shown in FIG. 1. FIG. 10 is an explanatory diagram of an operation for aligning a registered document image and an input document image based on a feature point of a registered document image and a feature point of an input document image that have matching feature amounts (hash values). FIG. 16 is an explanatory diagram showing a correspondence relationship between the coordinates of the feature points of the registered document image and the coordinates of the feature points of the input document image obtained as a result of alignment between the registered document image and the input document image shown in FIG. 15. When the registered document image is similar to one document image of the 2-in-1 input document image, 4 of the registered document image is obtained using the conversion coefficient obtained from the positional relationship of the feature points having the same feature value (hash value). It is explanatory drawing which shows the image which converted the coordinate of the corner into the coordinate on an input original image. In both FIGS. 18A to 18D, a registered document image similar to one document image of a 2-in-1 input document image is obtained from the positional relationship between feature points having the same feature value (hash value). It is explanatory drawing which shows the image of position shift at the time of converting into the coordinate on an input original image using a conversion factor. When the registered document image is similar to one document image of the 4in1 input document image, 4 of the registered document image is obtained by using the conversion coefficient obtained from the positional relationship of the feature points having the matching feature amount (hash value). It is explanatory drawing which shows the image which converted the coordinate of the corner into the coordinate on an input original image. In both FIG. 20A and FIG. 20B, the output processing (copying) when the input document image is that of the allocated document and one of the allocated document images is similar to the registered document image. It is explanatory drawing which shows an example. 3 is a flowchart showing operations in a registration mode and a collation mode of the image collation apparatus shown in FIG. 1. FIG. 2 is a block diagram illustrating a configuration of a digital color multifunction peripheral that is an image data output processing apparatus including the image collating apparatus of FIG. It is a block diagram which shows the structure of the color image reading apparatus which is an image data output processing apparatus provided with the image collation apparatus of FIG. It is explanatory drawing which shows the conventional subject, and is explanatory drawing which shows the example of an output process (copying) when the input original image is an allocation original, and one of several allocated original images resembles a registration original image It is.
DESCRIPTION OF SYMBOLS 1 Control part 2 Document collation process part 3 Memory (memory | storage means)
DESCRIPTION OF SYMBOLS 11 Feature point calculation part 12 Feature-value calculation part 13 Voting process part 14 Document similarity determination process part 15 Registration process part 16 Original discrimination | determination process part (original discrimination part)
101 Image Collation Device 102 Digital Color Copier (Image Data Output Processing Device)
103 Digital color MFP (image data output processing device)
104 color image reading device (image data output processing device)
112 Color Image Processing Device 116 Color Image Processing Device 117 Color Image Processing Device 123 Document Collation Processing Unit 126 Editing Processing Unit (Output Processing Control Unit)
A feature point calculation unit that calculates a feature point of the input document image from input image data of the input document, and a relative position between the feature points calculated by the feature point calculation unit, A feature amount calculation unit that calculates a feature amount, and the feature amount of the input document image calculated by the feature amount calculation unit and the feature amount of the registered document image are compared. In an image collation apparatus provided with a similarity determination unit that determines whether or not they are similar,
If the similarity determination unit determines that the input document images are similar, the input document image is based on the coordinate positions of the feature points of the input document image and the feature points of the registered document image that have matching feature amounts. A document determination unit that identifies the position of the registered document image above and determines whether or not the input document image is that of the allocated document using the position information ;
When the similarity determination unit determines that the document determination unit is similar, the document determination unit is based on the coordinate positions of the feature points of the input document image and the feature points of the registered document image that have matching feature amounts. A coefficient calculation unit that calculates a coefficient representing a positional relationship between the feature points of the input document image and the feature points of the registered document image;
When the coordinates of the reference point of the registered document image are converted into the coordinates of the input document image using the coefficient calculated by the coefficient calculation unit, and the converted reference point value satisfies a predetermined condition An image collating apparatus comprising: an assignment determination unit that determines that the input document image is that of an assigned document .
Converting the coordinates of the reference point of the registered document image into the coordinates of the input document image using the coefficient calculated by the coefficient calculation unit, and the converted reference point value satisfies a predetermined condition; and The registered original image in the input original image obtained from the size of the image area obtained from the coordinates of the reference point in the registered original image and the value of the reference point converted into the coordinates on the input original image. A layout determination unit that determines that the input document image is that of a layout document when a result of comparison with the size of the image area of a portion similar to the above satisfies a predetermined condition Image matching device.
Reference point of the reference document image, an image collating apparatus according to claim 1 or 2, characterized in that four corners each point of the reference document image.
An image data output processing device that performs an output process on input image data of an input document,
Comprising an image collating apparatus according to any one of claim 1 to item 3,
An output processing control unit for controlling output processing for the image data of the input document based on the determination result of the image collating device;
An image data output processing apparatus, wherein the output processing control unit performs control according to each allocated document image when the input document image is of an allocated document.
Based on the feature point calculation step of calculating the feature points of the input document image from the input image data of the input document and the relative positions of the feature points calculated in the feature point calculation step, the input document image A feature amount calculating step for calculating a feature amount, and comparing the feature amount of the input document image calculated in the feature amount calculation step with the feature amount of the registered document image, thereby converting the input document image into a registered document image. In an image matching method including a similarity determination step for determining whether or not they are similar,
If it is determined in the similarity determination step that the images are similar to each other, the input document image is based on the coordinate positions of the feature points of the input document image and the feature points of the registered document image having the same feature quantity. A document determination step of determining a position of the registered document image on the top and determining whether or not the input document image is of an allocated document using the position information ;
If it is determined in the similarity determination step that the document determination step is similar, the document determination step is based on the coordinate positions of the feature points of the input document image and the feature points of the registered document image that have matching feature amounts. A coefficient calculating step for calculating a coefficient representing a positional relationship between the feature points of the input document image and the feature points of the registered document image;
When the coordinates of the reference point of the registered document image are converted into the coordinates of the input document image using the coefficient calculated in the coefficient calculation step, and the converted reference point value satisfies a predetermined condition And an assignment determination step for determining that the input document image belongs to an assigned document .
If it is determined in the similarity determination step that the images are similar to each other, the input document image is based on the coordinate positions of the feature points of the input document image and the feature points of the registered document image having the same feature quantity. A document determination step of determining a position of the registered document image on the top and determining whether or not the input document image is of an allocated document using the position information;
Converting the coordinates of the reference point of the registered document image into the coordinates of the input document image using the coefficient calculated in the coefficient calculating step, and the converted reference point value satisfies a predetermined condition; and The registered original image in the input original image obtained from the size of the image area obtained from the coordinates of the reference point in the registered original image and the value of the reference point converted into the coordinates on the input original image. And a layout determination step for determining that the input document image is that of a layout document when a result of comparison with the size of the image area of a portion similar to the above satisfies a predetermined condition. Image matching method.
The program for functioning a computer as said each part of the image collation apparatus of any one of Claims 1-3 .
JP2008120256A 2008-05-02 2008-05-02 Image collation method, image collation apparatus, image data output processing apparatus, program, and storage medium Active JP4538507B2 (en)
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US12/432,381 US7974474B2 (en) 2008-05-02 2009-04-29 Method for matching images, image matching device, image data output apparatus, and recording medium
CN 200910137389 CN101571698B (en) 2008-05-02 2009-04-29 The image collation method, the image control apparatus, and an image data output processing apparatus
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