Image Processing Device

Abstract

To provide an image processing apparatus for improving the accuracy of identifying a corresponding area.SOLUTION: An image processing apparatus comprises a control unit that performs: processing for dividing first drawing data into a plurality of first partial regions from a first region representing an image of one page and dividing second drawing data into a plurality of second partial regions from a second region representing the image of one page; processing for generating, when a first characteristic amount of one of the two adjacent specific first partial regions is calculated, a first extended region in which a first predetermined region of a uniform color is set on an outer periphery of one of the specific first partial regions if the other specific first partial region interferes with the calculation of a first characteristic amount; processing for generating, when a second characteristic amount of one of the two adjacent specific second partial regions is calculated, a second extended region in which a second predetermined region of a uniform color is set on an outer periphery of one of the specific second partial regions if the other specific second partial region interferes with the calculation of the second characteristic amount; and processing for comparing the first extended region with the second extended region to identify the second extended region corresponding to the first extended region.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an image processing device. [Background technology]

[0002] There is known an image processing device that compares a pre-append image with a post-append image and extracts post-append information from the post-append image. In this image processing device, a white area is searched for in the image, and the entire image is divided into a plurality of areas based on the white area. Then, the pre-append image and the post-append image are relatively aligned for each divided area, and after the alignment, the post-append information is extracted from the post-append image (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-335920 Summary of the Invention [Problem to be solved by the invention]

[0004] When the relative positioning of the pre-addition image and the post-addition image is performed for each divided region (hereinafter referred to as a partial region), the correspondence between the pre-addition image and the post-addition image may be determined based on the comparison results of the feature amounts of each partial region. For example, the feature amount is calculated based on the pixels included in the target region, which determines the range for calculating the feature amount, by moving the target region over the specified partial region that is the target of feature amount calculation.

[0005] However, when the region of interest moves over a predetermined partial region, the region of interest may overlap another partial region adjacent to the predetermined partial region. As a result, the feature values ​​are calculated based on the pixels of the predetermined partial region and the pixels of the other partial region, which may result in a decrease in the accuracy of the feature values. If an attempt is made to identify the correspondence between the pre-addition image and the post-addition image based on such low-accuracy feature values, it may be impossible to accurately identify the corresponding partial regions.

[0006] Therefore, an object of the present invention is to provide an image processing device that improves the accuracy of identifying a corresponding partial region. [Means for solving the problem]

[0007] The image processing device of the present invention has a control unit that executes the following processes: dividing first drawing data into a plurality of first partial regions from a first region representing an image of one page of the first drawing data, and dividing second drawing data into a plurality of second partial regions from a second region representing an image of one page of the second drawing data; generating a first extended region in which a first predetermined region of a uniform color is set on the outer periphery of one of the specific first partial regions when calculating a first feature value of one of two specific adjacent first partial regions in the plurality of first partial regions, if the other specific first partial region interferes with the calculation of the first feature value; generating a second extended region in which a second predetermined region of a uniform color is set on the outer periphery of one of the specific second partial regions when calculating a second feature value of one of two specific adjacent second partial regions in the plurality of second partial regions, if the other specific second partial region interferes with the calculation of the second feature value; and comparing the first extended region with the second extended region and identifying the second extended region that corresponds to the first extended region based on the comparison result.

[0008] In the above configuration, the control unit may execute a process to generate the first extended area and the second extended area when a first positional relationship between one and the other of the specific first partial areas and a second positional relationship between one and the other of the specific second partial areas are relatively different.

[0009] In the above configuration, the control unit may execute a process of identifying the second extended region corresponding to the first extended region based on a first feature point possessed by the first extended region, a second feature point possessed by the second extended region, and a predetermined feature point matching method using machine learning.

[0010] In the above configuration, the control unit may perform processing to generate the first extended area by setting the first predetermined area to a size that avoids interference with the calculation of the first feature by the other of the specific first partial areas, and to generate the second extended area by setting the second predetermined area to a size that avoids interference with the calculation of the second feature by the other of the specific second partial areas.

[0011] In the above configuration, the first predetermined area and the second predetermined area may both be blank spaces. [Effects of the Invention]

[0012] According to the present invention, it is possible to improve the accuracy of identifying the corresponding partial region. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is an example of a hardware configuration of an image processing device. [Figure 2] 1 is an example of a functional configuration of an image processing device. [Figure 3] (a) is an example of first medium data, and (b) is an example of second medium data. [Figure 4] 10 is a flowchart illustrating an example of the operation of a control unit. [Figure 5] 1A is a diagram illustrating a case where different feature amounts are calculated, FIG. 1B is a diagram illustrating generation of an extension region, and FIG. 1C is a diagram illustrating comparison between extension regions. [Figure 6] (a) is an example of pairing consecutive regions based on the number of features. (b) is an example of pairing consecutive regions based on the distance between features. [Figure 7](a) is an example of a difference image, and (b) is another example of a difference image. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0015] First, the hardware configuration of the image processing device 100 will be described with reference to Fig. 1. The image processing device 100 includes a CPU (Central Processing Unit) 100A as a processor, and a RAM (Random Access Memory) 100B and a ROM (Read Only Memory) 100C as memories. The image processing device 100 also includes a network I / F (Interface) 100D and an HDD (Hard Disk Drive) 100E. An SSD (Solid State Drive) may be used instead of the HDD (Hard Disk Drive) 100E.

[0016] The image processing device 100 may include at least one of an input I / F 100F, an output I / F 100G, an input / output I / F 100H, and a drive device 100I, as necessary. The CPU 100A to the drive device 100I are connected to each other via an internal bus 100J. In other words, the image processing device 100 can be realized by a computer.

[0017] An input device 11 is connected to the input I / F 100F. Examples of the input device 11 include a keyboard, a mouse, and a touch panel. A display device 12 is connected to the output I / F 100G. Examples of the display device 12 include an LCD display. A semiconductor memory 13 is connected to the input / output I / F 100H. Examples of the semiconductor memory 13 include a USB (Universal Serial Bus) memory and a flash memory. The input / output I / F 100H reads a predetermined program stored in the semiconductor memory 13. The input I / F 100F and the input / output I / F 100H each include, for example, a USB port. The output I / F 100G includes, for example, a display port.

[0018] A portable recording medium 14 is inserted into the drive device 100I. The portable recording medium 14 may be a removable disk such as a CD (Compact Disc)-ROM or a DVD (Digital Versatile Disc). The drive device 100I reads a predetermined program recorded on the portable recording medium 14. The network I / F 100D includes, for example, a LAN (Local Area Network) port and a communication circuit.

[0019] A predetermined program stored in at least one of ROM 100C, HDD 100E, and semiconductor memory 13 is temporarily stored in RAM 100B by CPU 100A. A predetermined program recorded on portable recording medium 14 is temporarily stored in RAM 100B by CPU 100A. By executing the stored predetermined program, CPU 100A realizes various functions described below and executes various processes described below. The predetermined program may be one that corresponds to the flowchart described below.

[0020] The functional configuration of the image processing device 100 will be described with reference to Figures 2 and 3. Note that Figure 2 shows the main functional parts of the image processing device 100.

[0021] As shown in Fig. 2, the image processing device 100 includes a storage unit 110, a control unit 120, an input unit 130, and an output unit 140. The storage unit 110 can be realized by the RAM 100B or HDD 100E described above. The control unit 120 can be realized by the CPU 100A described above. The input unit 130 can be realized by the input I / F 100F described above. The output unit 140 can be realized by the output I / F 100G described above. Therefore, the storage unit 110, the control unit 120, the input unit 130, and the output unit 140 are connected to one another.

[0022] Here, the storage unit 110 includes a first medium storage unit 111, a second medium storage unit 112, and a display drawing storage unit 113. At least one of the first medium storage unit 111, the second medium storage unit 112, and the display drawing storage unit 113 may be provided in an information processing device (not shown) different from the image processing device 100. In this case, the image processing device 100 may access the information processing device and refer to the stored contents of the first medium storage unit 111, the second medium storage unit 112, etc.

[0023] The first medium storage unit 111 stores first medium data. The first medium data is a data file or an electronic file of an information transmission medium. The information transmission medium includes documents such as reports and instructions. In this embodiment, as shown in FIG. 3(a), the first medium data 50 is a document created on multiple pages, but it may also be a document created on a single page. When the first medium data 50 is a document created on multiple pages, depending on the document, it may include multiple pieces of drawing data 51, 52, or one piece of drawing data 53. The drawing data 51, 52, 53 are, for example, blueprints of finished products or parts. The shapes of the drawing data 51, 52, 53 are different from each other.

[0024] The second medium storage unit 112 stores the second medium data. Like the first medium data 50, the second medium data is a data file or an electronic file of an information transmission medium. In this embodiment, as shown in FIG. 3(b), the second medium data 60 is also a document created with multiple pages, but it may also be a document created with a single page. The number of pages of the second medium data 60 may be the same as or different from the number of pages of the first medium data 50. When the second medium data 60 is a document created with multiple pages, depending on the document, it may include one drawing data 61 or multiple drawing data 62, 63. The drawing data 61, 62, 63 include, for example, blueprints of finished products or parts. The shapes of the drawing data 61, 62, 63 are different from each other.

[0025] The above-mentioned first medium data 50 is stored in the first medium storage unit 111 based on an operation on the input device 11. The second medium data 60 is similarly stored in the second medium storage unit 112. In this embodiment, the first medium data 50 represents a comparison source for the second medium data 60, and the second medium data 60 represents a comparison destination or comparison object for the first medium data 50. Therefore, hereinafter, the drawing data 51, 52, 53 will be referred to as base drawings 51, 52, 53 serving as comparison sources, and the drawing data 61, 62, 63 will be referred to as new drawings 61, 62, 63 serving as comparison destination or comparison object.

[0026] The display drawing storage unit 113 stores images of drawings to be displayed on the display device 12. As will be described in detail later, the display drawing storage unit 113 stores an image of a drawing in which a base drawing image corresponding to the above-mentioned base drawings 51 and 52 is superimposed on a new drawing image corresponding to any two of the new drawings 61, 62, and 63 corresponding to the base drawing image, and portions corresponding to the differences are expressed in different colors. The display drawing storage unit 113 also stores an image of a drawing in which a base drawing image corresponding to the above-mentioned base drawing 53 is superimposed on a new drawing image corresponding to any one of the new drawings 61, 62, and 63 corresponding to the base drawing image, and portions corresponding to the differences are expressed in different colors. The base drawing image is an example of first drawing data, and the new drawing image is an example of second drawing data. Both the base drawing image and the new drawing image are image data.

[0027] Returning to FIG. 2, the control unit 120 includes an image generation unit 121, an image processing unit 122, and a display unit 123.

[0028] The image generation unit 121 acquires the first medium data 50 from the first medium storage unit 111 and images the first medium data 50. That is, the image generation unit 121 generates a first medium image representing the first medium data 50 from the acquired first medium data 50. If the first medium data 50 is a document created with multiple pages, the first medium image also includes multiple page images. The multiple page images included in the first medium image include the base drawing image described above.

[0029] Similarly, the image generation unit 121 acquires the second medium data 60 from the second medium storage unit 112 and images the second medium data 60. That is, the image generation unit 121 generates a second medium image representing the second medium data 60 from the acquired second medium data 60. If the second medium data 60 is a document created with multiple pages, the second medium image also includes multiple page images. The multiple page images included in the second medium image include the new drawing image described above.

[0030] The image processing unit 122 performs various image processing operations on the first medium image, the second medium image, the base drawing image, and the new drawing image. Details of the image processing unit 122 will be described later. For example, the image processing unit 122 compares multiple page images included in the first medium image with multiple page images included in the second medium image to identify page images that are similar to each other. The image processing unit 122 also divides the area representing the page image into multiple partial areas and calculates a first feature value for one of two specific partial areas adjacent to each other among the multiple partial areas. If the other specific partial area interferes with the calculation of the first feature value, the image processing unit 122 generates an extended area in which a predetermined area of ​​a uniform color is set on the periphery of one of the specific partial areas. The predetermined area of ​​a uniform color is preferably a margin that is the same color as the background, but may be a color other than white (e.g., red or blue). Furthermore, the image processing unit 122 identifies the corresponding extended areas based on a known feature point matching method using machine learning. The shape of the extended area may be rectangular or may be a shape other than a rectangle (e.g., circular). Additionally, the image processing unit 122 monochromeizes (specifically, monochrome binarizes) each of the base drawing image and the new drawing image, and assigns different colors to each of the base drawing image and the new drawing image.

[0031] As a known feature point matching method, for example, the following documents 1 and 2 can be referred to. Reference 1: PF Alcantarilla, A. Bartoli, and AJ Davison. “KAZE features”, ECCV, (2012), pp.214?227. Reference 2: P. F, Alcantarilla, J. Nuevo, A. Bartoli, “Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces”, BMVC, (2013), pp.13.1-13.11.

[0032] The display unit 123 acquires an image of the drawing stored in the display drawing storage unit 113 and displays the acquired drawing on the display device 12. As described above, the display drawing storage unit 113 stores, for example, an image of the drawing in which parts corresponding to the differences between the base drawing image and the new drawing image are expressed in different colors. Therefore, the display unit 123 acquires and displays this drawing.

[0033] Next, the processing executed by the control unit 120 will be described with reference to FIGS.

[0034] 4, the image generation unit 121 first generates an image of the medium data (step S1). More specifically, the image generation unit 121 acquires the first medium data 50 from the first medium storage unit 111 and generates a first medium image. The image generation unit 121 also acquires the second medium data 60 from the second medium storage unit 112 and generates a second medium image.

[0035] Once the medium data has been imaged, the image processing unit 122 identifies similar pages (step S2). Specifically, the image processing unit 122 compares the multiple page images included in the first medium image with the page images included in the second medium image for each page image, and calculates the similarity between the page images. After calculating the similarity, the image processing unit 122 identifies the page images with the highest calculated similarity as similar pages. This allows similar pages to be uniquely identified. In this embodiment, for example, page images including the characters "FIG. 1" are identified as similar pages (see FIGS. 3(a) and (b)).

[0036] When a similar page is identified, the image processing unit 122 divides the one region into partial regions (step S3). That is, the image processing unit 122 divides each of the two page images identified as similar pages into a plurality of partial regions from a single region corresponding to the entire page image. After performing the region division, for each of the two page images identified as similar pages, the image processing unit 122 leaves one of two specific partial regions adjacent to each other among the plurality of partial regions and deletes the other specific partial region (step S4).

[0037] More specifically, the image processing unit 122 first identifies two specific adjacent partial areas among the multiple partial areas. For example, as shown in FIG. 5A, the image processing unit 122 identifies partial area 71, which is one of the two specific partial areas originating from the comparison source page image, and partial area 72, which is the other of the two specific partial areas. Partial area 71 includes base drawing 52, and partial area 72 also includes base drawing 51. Similarly, the image processing unit 122 identifies partial area 81, which is one of the two specific partial areas originating from the comparison target page image, and partial area 82, which is the other of the two specific partial areas. Partial area 81 includes new drawing 63, and partial area 82 includes new drawing 62.

[0038] Here, when image processing unit 122 moves a circular region of interest IR, which defines the range for calculating feature amounts, over partial regions 71 and 81 and calculates feature amounts based on the pixels included in region of interest IR, different feature amounts may be calculated. For example, when region of interest IR moves over partial region 71, part of partial region 72 may be included in region of interest IR. In this case, partial region 72 interferes with the calculation of the feature amount of partial region 71. In other words, the feature amount calculated by image processing unit 122 includes not only the feature amount of partial region 71 but also the feature amount of part of partial region 72.

[0039] Similarly, when the region of interest IR moves over the partial region 81, a portion of the partial region 82 may be included in the region of interest IR. In this case, the partial region 82 interferes with the calculation of the feature amount of the partial region 81. That is, the feature amount calculated by the image processing unit 122 includes not only the feature amount of the partial region 81 but also the feature amount of a portion of the partial region 82. In particular, as shown in FIG. 5A, if the positional relationship between the partial regions 71 and 72 and the positional relationship between the partial regions 81 and 82 are relatively different, the feature amounts calculated by the image processing unit 122 may differ significantly. For example, if these positional relationships are relatively different, the shape of a portion of the partial region 72 included in the region of interest IR differs from the shape of a portion of the partial region 82 included in the region of interest IR. In the example of FIG. 5A, the corner portion of the partial region 82 is included in the region of interest IR, but the corner portion of the partial region 72 is not included in the region of interest IR. This causes a difference in the feature amount of a portion of the partial region 72 and the feature amount of a portion of the partial region 82, resulting in a large difference in the feature amounts calculated by the image processing unit 122.

[0040] 5(b), the image processing unit 122 leaves the partial region 71 and deletes the partial region 72. Also, although not shown, similar to the case of the partial regions 71 and 72, the image processing unit 122 leaves the partial region 81 and deletes the partial region 82.

[0041] When the processing of step S4 is completed, the image processing unit 122 sets a predetermined region and generates an extended region (step S5). For example, as shown in Fig. 5(b), the image processing unit 122 generates extended region 92 by setting a predetermined region 91 of a uniform color on the outer periphery of partial region 71. Furthermore, as in the case of partial region 71, the image processing unit 122 generates extended region 96 by setting a predetermined region 95 of a uniform color on the outer periphery of partial region 81.

[0042] The predetermined region 91 is larger than the partial region 71 and is sized to avoid interference with the calculation of feature amounts caused by the partial region 72. Furthermore, the predetermined region 95 is larger than the partial region 81 and is sized to avoid interference with the calculation of feature amounts caused by the partial region 82. As a result, as shown in FIG. 5(b), even if the region of interest IR moves over the extended region 92, it is possible to avoid interference caused by the partial region 72. Although not shown, even if the region of interest IR moves over the extended region 96, it is possible to avoid interference caused by the partial region 82.

[0043] When the processing of step S5 is completed, the image processing unit 122 uses the feature point matching method described above to identify the correspondence between the extended regions (step S6). More specifically, the image processing unit 122 uses the feature point matching method to identify the correspondence between the comparison source extended region 92 and the comparison target extended region 96, as shown in FIG. 5(c). The identification of the correspondence includes a first stage and a second stage. The order of the first stage and the second stage is not particularly limited, and either the first stage or the second stage may be performed first.

[0044] First, the first stage will be described. When the comparison source extended region has a plurality of points (for example, pixels, or a local or minute region including a plurality of pixels as elements) A, X, etc., and the comparison target extended region has a plurality of points B, C, Y, etc., the image processing unit 122 calculates, in the first stage, the distance L(A,B) between points A and B and the distance L(A,C) between points A and C. Each of points A, B, C, X, Y, etc. has a plurality of components (for example, lightness, saturation, luminance, average of pixel values ​​(or RGB values), variance of pixel values, histogram of pixel values, etc.) as feature amounts. Therefore, points A and X may be called first feature points, and points B, C, Y may be called second feature points. For example, point X has lightness as component x1, saturation as component x2, ..., component x n The points A, B, C, and Y are basically the same as the point X, so detailed explanations will be omitted.

[0045] As a result, the image processing unit 122 can calculate, for example, the distance L(X,Y) using the following calculation formula: Similarly, the image processing unit 122 can also calculate the distance L(A,B) and the distance (A,C) using this calculation formula. Based on the distance L(X,Y) and the distance L(A,B), the image processing unit 122 can determine the similarity between the feature amount of point X and the feature amount of point Y, and the similarity between the feature amount of point A and the feature amount of point B. <Calculation formula> Distance L(X,Y)={(x1-y1) 2 +(x2-y2) 2 +···+(x n -y n ) 2}} 0.5

[0046] Here, the image processing unit 122 uses the number of pairs (hereinafter referred to as element pairs) with unique feature amounts of elements in the extended area to select candidates for extended areas (hereinafter referred to as pairing candidates) for identifying correspondence (pairing). Specifically, the image processing unit 122 selects, as pairing candidates, extended areas that have the largest number of point pairs with matching feature amounts of points within the extended area. The image processing unit 122 determines that the feature amounts match when the distance L between two points is the smallest compared to the distance L between other two points.

[0047] An element pair having a unique feature is required to satisfy the following inequality, based on, for example, the distance L(A,B) between point A and point B, which is the shortest distance and a candidate pair for point A, and the distance L(A,C) between point A and point C, which is the next shortest distance after point B and a candidate pair for point A. If the distance L(A,B) between point A in the source extended region and point B in the target extended region is the shortest, the feature values ​​of points A and B are very similar, and the image processing unit 122 can determine that the feature values ​​match. The first threshold can be set to a positive value less than 1.0, such as 0.5 or 0.6. <Inequalities> Distance L(A,B) / Distance L(A,C)<First threshold

[0048] As a result, as shown in FIG. 6(a), the image processing unit 122 can select pairing candidates for a plurality of corresponding extended areas (referred to as areas in FIG. 6(a)) #1 to #5. For example, the candidates for the extended area corresponding to the extended area #3 of the base drawing image are the candidates for the extended area #2 and the extended area #3 of the new drawing image. Here, the number of point pairs in which the feature amounts of the points in the extended area #3 of the base drawing image and the points in the extended area #2 of the new drawing image match is 80. On the other hand, the number of point pairs in which the feature amounts of the points in the extended area #3 of the base drawing image and the points in the extended area #3 of the new drawing image match is 40. Therefore, the image processing unit 122 determines the extended area #3 of the base drawing image and the extended area #2 of the new drawing image, which have the larger number of point pairs, as pairing candidates for the extended areas.

[0049] Next, the second stage will be described. In the second stage, the image processing unit 122 selects pairing candidates for the extended region that identify correspondence relationships using the shortest distances between the feature amounts of the elements in the extended region. For example, the image processing unit 122 selects several to a dozen (e.g., 5, 10, or 15) point pairs in ascending order of distance L, and selects pairing candidates if the average value obtained by dividing the sum of the distances L of the selected point pairs by the number of points is less than a second threshold.

[0050] Here, when several tens of numerical values ​​such as a threshold value of "30" or a threshold value of "50" are set as the second threshold value, the image processing unit 122 can select pairing candidates of a plurality of corresponding extension areas (described as areas in FIG. 6(b)) #1 to #5, as shown in FIG. 6(b). For example, the extension area candidates corresponding to extension area #3 of the base drawing image are extension area candidates #2 and #3 of the new drawing image.

[0051] Here, the average value of the distance L between the points in the extended area #3 of the base drawing image and the points in the extended area #2 of the new drawing image is "10." On the other hand, the average value of the distance L between the points in the extended area #3 of the base drawing image and the points in the extended area #3 of the new drawing image is "13." Therefore, the image processing unit 122 determines the extended area #3 of the base drawing image and the extended area #2 of the new drawing image, which have a short average value of the distance L, as pairing candidates for the extended areas. Note that the image processing unit 122 may select pairing candidates using the above-mentioned sum and a fifth threshold value greater than the above-mentioned second threshold value, without using the average value.

[0052] The image processing unit 122 finally determines a pair of the comparison source extended area and the comparison destination extended area based on the pairing candidates determined in the first stage and the pairing candidates determined in the second stage. Therefore, as shown in Figures 6(a) and 6(b), the image processing unit 122 pairs, for example, extended area #1 of the base drawing image with extended area #1 of the new drawing image as extended areas having a corresponding relationship.

[0053] As shown in FIG. 6(a), the image processing unit 122 selects the extended area #5 of the base drawing image and the extended area #5 of the new drawing image as pairing candidates, but as shown in FIG. 6(b), the image processing unit 122 excludes the extended area #5 of the base drawing image and the extended area #5 of the new drawing image from the selection of pairing candidates. This is because the average value of the distance L between the extended area #5 of the base drawing image and the extended area #5 of the new drawing image exceeds the second threshold. In this case, the image processing unit 122 may determine that the extended area #5 has been deleted from the base drawing image and added to the new drawing image.

[0054] In this way, the image processing unit 122 pairs the extended regions with each other by identifying the correspondence relationship including the first and second stages. That is, the image processing unit 122 prevents a partial region included in the new drawing image as the comparison target from being determined as a pairing target due to low-accuracy features of a partial region included in the base drawing image as the comparison source. In this way, by identifying and generating the extended regions, the image processing unit 122 can accurately and uniquely identify the correspondence relationship between the extended regions and accurately identify the correspondence relationship between the partial regions. Note that although the extended regions of drawings have been described as an example, the image processing unit 122 may also identify and generate extended regions for characters in the same way as for drawings to identify the correspondence relationship.

[0055] Once the correspondence has been identified, the image processing unit 122 moves one of the extended regions for which the correspondence has been identified to the other (step S7), thereby aligning the positions of the base drawing image and the new drawing image (step S8). Once the positions of the base drawing image and the new drawing image have been aligned, the image processing unit 122 converts the base drawing image to monochrome and assigns green alone. Green is one of the three primary colors of light, and can be expressed as (R, G, B) = (0, 255, 0) in a color representation of brightness classified into 256 gradations in the RGB format. In the brightness classified into 256 gradations, the numerical data "255" represents the brightest brightness. By converting the base drawing image to monochrome and assigning green alone, the base drawing image can be expressed in green.

[0056] When the base drawing image is monochromatized and green is added alone, the image processing unit 122 monochromatizes the new drawing image and adds red alone. Red is also one of the three primary colors of light, and can be expressed as (R, G, B) = (255, 0, 0) in RGB color representation. By monochromatizing the new drawing image and adding red alone, the new drawing image can be expressed in red. Note that green may be added to the new drawing image, and red may be added to the base drawing image.

[0057] When the new drawing image is converted to monochrome and red is added alone, the image processing unit 122 overlays the base drawing image and the new drawing image on a blue background. Blue is one of the three primary colors of light, and can be expressed as (R, G, B) = (0, 0, 255) in RGB color representation. The background is an example of predetermined data. When the base drawing image and the new drawing image are overlaid on a blue background, the result is (R, G, B) = (255, 255, 255), which allows white to be expressed.

[0058] In this way, the portion of the white background that contains only the green base drawing image can be expressed as magenta, including pink, because (R,G,B) = (255,255,255) - (0,255,0) = (255,0,255). Similarly, the portion of the white background that contains only the red new drawing image can be expressed as cyan, including light blue, because (R,G,B) = (255,255,255) - (255,0,0) = (0,255,255). Furthermore, the overlapping portion of the white background containing the green base drawing image and the red new drawing image can be expressed as blue, because (R,G,B) = (255,255,255) - (0,255,0) - (255,0,0) = (0,0,255).

[0059] When the base drawing image and the new drawing image are superimposed on the blue background, the image processing unit 122 stores such an image of the drawing in the display drawing storage unit 113. Once the image of the drawing is stored in the display drawing storage unit 113, the display unit 123 displays the image of the drawing on the display device 12 (step S9), and the processing ends. As a result, as shown in Figures 7(a) and (b), the base drawing image and the new drawing image are superimposed, and an image of the drawing that is partially expressed in different colors appears on the display device 12.

[0060] 7(a), the difference between the base drawing image corresponding to the base drawing 51 and the new drawing image corresponding to the new drawing 62, and the difference between the base drawing image corresponding to the base drawing 52 and the new drawing image corresponding to the new drawing 63, appear as difference images P1 and P2 of different colors on the display device 12. In this case, the difference image P1, which is a part of the base drawing image, is displayed in magenta, and the difference image P2, which is a part of the new drawing image, is displayed in cyan. Then, the superimposed image P3, which is the superimposed portion of the base drawing image and the new drawing image, is displayed in blue.

[0061] 7(b), the difference between the base drawing image corresponding to the base drawing 53 and the new drawing image corresponding to the new drawing 61 appears as a difference image Q1 of a different color on the display device 12. In this case, the difference image Q1, which is a part of the base drawing image, is displayed in magenta, but since the entire new drawing image is superimposed on the base drawing image, the new drawing image is displayed as a blended image in blue as a superimposed image Q3.

[0062] As described above, in the image processing device 100 according to this embodiment, for each of two page images identified as similar pages, the image processing unit 122 calculates the feature amounts of two adjacent specific partial regions 71, 81. If the partial regions 72, 82 interfere with the feature amount calculation, the image processing unit 122 generates extended regions 92, 96, each of which includes a uniformly colored predetermined region 91, 95 on the periphery of the partial region 71, 81, and identifies the correspondence between the extended regions 92, 96. This reduces the low accuracy of identifying the partial regions 71, 81, which occurs when the extended regions 92, 96 are not generated. As a result, the accuracy of identifying the partial region 71 included in the extended region 92 of the comparison source and the partial region 81 included in the extended region 96 of the comparison target can be improved.

[0063] Although the preferred embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications and variations are possible within the scope of the gist of the present invention as described in the claims. [Explanation of symbols]

[0064] 50 First Media Data 51, 52, 53 Base drawings 60 Second Media Data 61,62,63 New drawings 71,72,81,82 partial area 91,95 Predetermined area 92,96 Expansion Area 100 Image processing device 120 control section IR Focus Areas

Claims

1. A process of dividing the first drawing data into a plurality of first partial regions from a first region representing an image of one page of the first drawing data, and dividing the second drawing data into a plurality of second partial regions from a second region representing an image of one page of the second drawing data; a process of generating a first extended region in which a first predetermined region of a uniform color is set on an outer periphery of one of the specific first partial regions when a first feature amount of one of two specific first partial regions adjacent to each other among the plurality of first partial regions is calculated and the other specific first partial region interferes with the calculation of the first feature amount; a process of generating a second extended region in which a second predetermined region of a uniform color is set on an outer periphery of one of the specific second partial regions when calculating a second feature amount of one of the two specific second partial regions adjacent to each other among the plurality of second partial regions and the other specific second partial region interferes with the calculation of the second feature amount; a process of comparing the first extension region with the second extension region and identifying the second extension region corresponding to the first extension region based on a comparison result; An image processing device having a control unit that executes the above.

2. the control unit generates the first extended region and the second extended region when a first positional relationship between one and the other of the specific first partial regions and a second positional relationship between one and the other of the specific second partial regions are relatively different.

2. The image processing apparatus according to claim 1, wherein the image processing apparatus executes processing.

3. the control unit specifies the second extended region corresponding to the first extended region based on a first feature point of the first extended region, a second feature point of the second extended region, and a predetermined feature point matching method using machine learning.

3. The image processing apparatus according to claim 1, wherein the image processing apparatus executes processing.

4. the control unit generates the first extended region in which the first predetermined region is set to a size that avoids interference with the calculation of the first feature amount by the other of the specific first partial regions, and generates the second extended region in which the second predetermined region is set to a size that avoids interference with the calculation of the second feature amount by the other of the specific second partial regions.

3. The image processing apparatus according to claim 1, wherein the image processing apparatus executes processing.

5. The first predetermined area and the second predetermined area are both blank spaces.

3. The image processing device according to claim 1, wherein the image processing device is a computer.

Images