Image processing device, image processing method, and program

Abstract

To suppress over-detection of abnormalities in portions of images, such as preprinted images, where variations tend to be easily detected in inspection of printed materials, with less burden on a user.SOLUTION: An image processing method disclosed herein comprises acquiring multiple printed images obtained by reading multiple printed materials, evaluating differences among the multiple printed images, and setting an inspection exclusion area for printed material inspection on the basis of a result of the evaluation.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an image processing apparatus, an image processing method, and a program, and particularly to a technique for inspecting printed matter.

Background Art

[0002] Printed matter output by a printing apparatus may have abnormalities (also referred to as defects) such as unintended stains, and thus such abnormalities are inspected to ensure the quality of the printed matter. Since inspection by visual observation of an inspector requires a lot of time and cost, in recent years, techniques for automating the inspection process have been developed.

[0003] In order to improve the productivity of printed matter, it is desirable to suppress over-detection of abnormalities in printed matter by the inspection process (determining a non-abnormal printed matter as abnormal). For example, Patent Document 1 proposes performing an inspection of a common image while excluding a region where a variable image is printed from inspection targets in the inspection of printed matter including a variable image different for each printed matter and a common image common to a plurality of printed matters. For this purpose, Cited Document 1 also proposes that a user specify a variable region to be excluded from inspection targets on a UI panel. Cited Document 1 suppresses detection of abnormalities in the variable image by comparing the reading result of each printed matter with the reading result of a specific printed matter that has already been confirmed to have no quality problems, and improves the yield rate of printed matter.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Printed materials may contain areas where image variations are easily detected. For example, printed materials are sometimes generated by printing on pre-printed paper that has already been pre-printed. In this case, when inspecting the printed material, anomalies in the pre-printed area may be over-detected due to misalignment between the pre-printed image and the printed image, even though the print quality is visually acceptable. The method described in Patent Document 1 does not address printing on pre-printed paper. Furthermore, excluding pre-printed images from inspection by user operation requires a complicated specification process.

[0006] The present invention aims to suppress the over-detection of abnormalities in areas of printed materials where image variations, such as pre-printed images, are easily detected, with minimal burden on the user during inspection. [Means for solving the problem]

[0007] The image processing apparatus according to one embodiment of the present invention has the following configuration. That is, An acquisition means for obtaining multiple printed images obtained by reading each of multiple printed materials, An evaluation means for evaluating the differences between the plurality of printed images, Based on the evaluation results by the evaluation means, a setting means for setting exclusion areas in the print inspection, Equipped with 、 The evaluation means determines the variance of color information at corresponding positions between the multiple printed images as a feature quantity indicating the magnitude of the difference. . [Effects of the Invention]

[0008] In print inspection, this system aims to suppress false positives of anomalies in areas where image variations are easily detected, such as pre-printed images, with minimal burden on the user. [Brief explanation of the drawing]

[0009] [Figure 1] A diagram showing an example of the configuration of a printing system according to one embodiment. [Figure 2]A block diagram showing an example of the functional configuration of an image processing apparatus according to one embodiment. [Figure 3] A diagram showing an example of a flowchart for an image processing method according to one embodiment. [Figure 4] A figure showing an example of the display of a UI panel in one embodiment. [Figure 5] A figure showing an example of an edge image used in one embodiment. [Figure 6] A figure showing an example of a histogram used in one embodiment. [Modes for carrying out the invention]

[0010] The embodiments will be described in detail below with reference to the attached drawings. Note that the following embodiments do not limit the invention as defined in the claims. While the embodiments describe multiple features, not all of these features are essential to the invention, and the features may be combined in any way. Furthermore, in the attached drawings, identical or similar configurations are given the same reference numerals, and redundant descriptions are omitted.

[0011] Figure 1 shows an example of the overall configuration of a printing system for outputting and inspecting printed materials, including an image processing device 100 according to one embodiment of the present invention. The printing system according to this embodiment includes an image processing device 100, a printing server 180, and a printing device 190. The printing server 180 generates a print job for the original document to be printed and submits the print job to the printing device 190. This print job may include electronic data of the original document to be printed, such as image data. Hereinafter, this electronic data may be referred to as print data. The printing device 190 forms an image on a recording medium (e.g., paper) based on the print job submitted from the printing server 180. In this example, printing paper is used as the recording medium. The printing device 190 has a paper feed unit 191, and the user can set the printing paper in the paper feed unit in advance. When a print job is submitted, the printing device 190 transports the printing paper set in the paper feed unit 191 along the transport path 192, forms an image according to the print job on its surface or both sides, and sends it to the image processing device 100.

[0012] An image processing apparatus 100 according to an embodiment of the present invention inspects a printed matter. In this example, the image processing apparatus 100 performs an inspection process for checking the presence or absence of abnormalities on the printing paper, that is, the printed matter, on which an image is formed by a printing apparatus 190 and sent through a conveyance path 192. Thus, the image processing apparatus 100 functions as an inspection processing apparatus. The image processing apparatus 100 can inspect the printed matter based on a comparison at each position between a printed image obtained by reading the printed matter and a reference image indicating an acceptable printing result. Specifically, the image processing apparatus 100 can determine that an abnormality exists at a position where the difference between the printed image and the reference image is large among the printed images.

[0013] The image processing apparatus 100 has a CPU 101, a RAM 102, a ROM 103, a main storage device 104, and an image reading device 105 inside. The image processing apparatus 100 further has an interface (I / F) 106 with the printing apparatus, a general-purpose interface (I / F) 107, a user interface (UI) panel 108, and a main bus 109. The image processing apparatus 100 further has a conveyance path 110 for the printed matter connected to the conveyance path 192 of the printing apparatus 190, an output tray 111 for discharging the printed matter that has passed the inspection without any abnormalities found, and an output tray 112 for discharging the printed matter that has failed the inspection with abnormalities found.

[0014] The CPU 101 is a processor and can comprehensively control each part inside the image processing apparatus 100. The RAM 102 can function as a main memory or a work area for the CPU 101. The ROM 103 stores a group of programs executed by the CPU 101. The main storage device 104 stores applications executed by the CPU 101 and data used for image processing. By a processor such as the CPU 101 executing a program stored in a memory such as the RAM 102, the ROM 103, or the main storage device 104, the functions of each part shown in FIG. 2 and the like described later can be realized.

[0015] The image reading device (scanner) 105 can read one side or both sides of a printed matter sent from the printing device 190 on the conveyance path 110 and acquire it as image data (printed image). The printing device I / F 106 is connected to the printing device 190. The printing device I / F 106 is used for communication with the printing device 190, and through the printing device I / F 106, it is possible to synchronize the processing timing of the printed matter with the printing device 190 and communicate the operating status with each other. The general-purpose I / F 107 is an interface for connecting an external device. The general-purpose I / F 107 is a serial bus interface such as USB or IEEE 1394, for example, and can be used for the user to take out data such as a log. The UI panel 108 is a user interface of the image processing device 100. The UI panel is, for example, a liquid crystal display, and can display to convey the current status or settings of the image processing device 100 to the user. Further, the UI panel 108 can be provided with a touch panel type display or buttons, and can receive instructions from the user through these input devices. The main bus 109 connects each part of the image processing device 100.

[0016] In addition, although omitted in FIG. 1, each part of the image processing device 100 or the printing system can be operated according to an instruction from the CPU 101. For example, it is possible to move the conveyance paths 110 and 192 synchronously, and to switch the discharge destination of the printed matter between the output tray 111 and the output tray 112 according to the inspection result.

[0017] As a whole, the image processing device 100 performs the inspection process described below based on the image data of the printed matter read by the image reading device 105 while conveying the printed matter sent from the printing device 190 on the conveyance path 110. If the printed matter passes the inspection, it is conveyed to the output tray 111, and if not, it is conveyed to the output tray 112. In this way, only the printed matter that has passed the inspection can be collected in the output tray 111 as the printed matter for delivery.

[0018] Figure 2 is a functional block diagram of the image processing apparatus 100 according to this embodiment. In this embodiment, the image processing apparatus 100 sets exclusion areas for inspection in print inspection by evaluating the differences between multiple printed images obtained by reading each of multiple printed materials. As described above, the functions of the image processing apparatus 100 shown in Figure 2 can be realized by a computer equipped with a processor and memory, but some or all of the functions of the image processing apparatus 100 may be realized by dedicated hardware. Furthermore, the image processing apparatus according to one embodiment of the present invention may be composed of multiple information processing apparatuses connected via a network, for example.

[0019] The image acquisition unit 201 acquires multiple printed images obtained by reading each of multiple printed materials. Each of these multiple printed materials may contain a common object. For example, each of the multiple printed materials may be obtained by printing the same image. Alternatively, each of the multiple printed materials may be a printed material on which a common object is printed on a medium that already has an image printed on it. In the following example, the printed materials are obtained by the printing device 190 printing on pre-printed paper according to a print job. Each of the multiple printed materials is obtained by printing the same image on the same type of pre-printed paper. The image acquisition unit 201 acquires the scanned image of the printed material read by the image reading device 105 as a printed image. The acquired printed image is stored in the main memory 104.

[0020] The alignment unit 202 aligns multiple print images. The alignment unit 202 can align print images held in the main memory 104. The alignment unit 202 can align multiple print images based on a common object. For example, the print data included in a print job may include alignment marks. In this case, each of the multiple printed materials and multiple print images includes a common alignment mark, and the print images can be aligned based on this alignment mark. The alignment unit 202 may also perform alignment based on images other than the alignment marks.

[0021] The evaluation unit 203 evaluates the differences between multiple printed images. The evaluation unit 203 can evaluate local differences between multiple printed images at each location. For example, the evaluation unit 203 can compare multiple printed images held in the main memory 104 and calculate a feature quantity for each pixel that indicates the variation in pixel values ​​among the multiple printed images.

[0022] In this example, the evaluation unit 203 evaluates the differences in the relative positions of multiple printed images with respect to a common object. Specifically, the evaluation unit 203 can evaluate the differences between multiple printed images after they have been aligned based on a common object by the alignment unit 202. As described above, when an object is printed on preprint paper, the relative position between the preprint image and the object may fluctuate. In this case, if alignment is performed based on the object, the position of the preprint image will fluctuate between the printed images. By evaluating the differences between multiple printed images as described above, the evaluation unit 203 can detect regions where the position of the image fluctuates, i.e., regions of the preprint image. The processing performed by the evaluation unit 203 will be explained in detail later.

[0023] The setting unit 204 sets exclusion areas for inspection in print inspection based on the evaluation results by the evaluation unit 203. For example, the setting unit 204 can set areas where the magnitude of the difference evaluated by the evaluation unit 203 is greater than a predetermined standard as exclusion areas. In the following example, the setting unit 204 can generate a mask image indicating the exclusion areas for inspection based on the variation in pixel values ​​calculated by the evaluation unit 203.

[0024] The display control unit 205 displays the exclusion areas set by the setting unit 204 on the display. For example, the display control unit 205 can output the exclusion areas to the UI panel 108.

[0025] The inspection unit 206 performs inspection of the printed material. The inspection unit 206 can perform inspection of the printed material based on a comparison between the image obtained by reading the printed material to be inspected and a reference image that shows an acceptable print result. The reference image is a scanned image of a printed material that has been confirmed to be free of abnormalities. At this time, the inspection unit 206 can align the image of the printed material to be inspected with respect to the reference image. This alignment can be performed based on objects common to both the reference image and the image of the printed material to be inspected. Then, the inspection unit 206 can perform inspection of the printed material based on a comparison of corresponding positions between the reference image and the image of the printed material to be inspected.

[0026] In this case, the inspection unit 206 can perform an inspection on the printed material to be inspected without inspecting the excluded areas. For example, the inspection unit 206 can perform an inspection on areas other than the excluded areas of the printed image without inspecting the excluded areas. Specifically, the inspection unit 206 can perform an inspection on the printed image held in the main memory 104 based on the excluded areas set by the setting unit 204 and output the inspection results. The inspection results output by the inspection unit 206 can be used for the process of switching the output destination of the printed material between the output tray 111 and the output tray 112.

[0027] The image of the printed material to be inspected may be one of several printed images acquired by the image acquisition unit 201, or it may be any other printed image. In other words, the image acquisition unit 201 may acquire an image obtained by reading the printed material to be inspected as at least one of several printed images. For example, the image acquisition unit 201 may acquire images of multiple printed materials to be inspected as multiple printed images, and the evaluation unit 203 may evaluate the differences between these images. On the other hand, the evaluation unit 203 may evaluate the differences between printed images of multiple printed materials that are different from the printed material to be inspected. In this case, the printed material to be inspected and the multiple printed materials acquired by the image acquisition unit 201 may be printed materials obtained by printing on the same type of preprint paper based on the same print data.

[0028] Figure 3 shows a flowchart of the image processing performed by the image processing device 100 according to one embodiment of the present invention. In S301, the image reading device 105 scans the printed material printed by the printing device 190 and generates a printed image. The image acquisition unit 201 then acquires this printed image and stores it in the main memory 104. In S301, the scanning of each of the multiple printed materials is repeated until a predetermined number of printed images are obtained.

[0029] In S302, the alignment unit 202 selects a reference image to be used as the alignment reference from the print images held in the main memory 104. The selection method is not particularly limited. The alignment unit 202 then refers to information indicating the location of common areas where objects common to multiple printed materials are printed, and extracts feature points from the common areas of the selected reference image and other printed images. As described above, these feature points may be, for example, alignment marks, or other points on the common image. In this way, the alignment unit 202 can align the printed images with respect to the reference image so that the feature points extracted from the reference image and the other printed images match.

[0030] In S303, the evaluation unit 203 evaluates the differences between printed images at each pixel of the reference image and other printed images that are aligned with each other. For example, the evaluation unit 203 can evaluate the differences in color information at corresponding positions between multiple printed images in order to evaluate the differences. The type of color information is not particularly limited, and the color information may be, for example, the pixel value or luminance value of each pixel, or the local contrast. In this embodiment, the evaluation unit 203 determines the variance of the color information at corresponding positions between multiple printed images as a feature quantity indicating the magnitude of the difference. Specifically, the evaluation unit 203 can calculate the unbiased variance of the pixel value at each pixel as a feature quantity indicating the difference. The method for calculating the unbiased variance will be described later.

[0031] In S304, the setting unit 204 compares the feature quantities calculated in S303 with a pre-set threshold and determines that pixels with feature quantities greater than the threshold belong to the exclusion region for inspection. The setting unit 204 performs this determination for each pixel and generates a mask image indicating the exclusion region for inspection. This mask image is a binary image in which the pixel values ​​of pixels determined to be in the exclusion region are set to 0, and the pixel values ​​of all other pixels are set to 1. The generated mask image is stored in the main memory 104.

[0032] In S305, the display control unit 205 outputs the mask image indicating the excluded area, generated in S304, to the UI panel 108. Figure 4 shows an example of the display on the UI panel 108 in this embodiment. Operation button 401 is a button for instructing the execution of the processes S301 to S304, which set the excluded area. Display screen 402 is a screen that displays the mask image 403 indicating the excluded area 404. The method of displaying the excluded area is not limited to displaying the mask image. For example, instead of the mask image 403, or in addition to the mask image 403, the display screen 402 may display a list of the coordinates of pixels determined to be in the excluded area.

[0033] Operation buttons 405 and 406 are buttons for the user to select whether to adopt or not adopt the exclusion areas shown on the display screen 402, respectively. In one embodiment, when the user confirms the exclusion areas shown on the display screen 402 and presses operation button 405, an inspection of the printed material according to the exclusion areas set in S304 is performed in S306. In a further embodiment, the exclusion areas set in S304 may be modified according to user input.

[0034] In S306, the inspection unit 206 performs an inspection on the image of the printed material to be inspected, excluding the exclusion areas set in S304 from the inspection target, and outputs the inspection results. The inspection unit 206 can perform an inspection on the printed image held in the main memory 104 that was used to generate the mask image, while also being able to perform an inspection on an image of another printed material newly acquired by the image acquisition unit 201.

[0035] As described above, the inspection unit 206 can align the image of the printed material to be inspected with respect to the reference image. This alignment can be performed in the same manner as in S302, and for example, alignment marks may be used. The inspection unit 206 can then calculate the difference in color information at corresponding positions between the reference image and the image of the printed material to be inspected.

[0036] Here, the inspection unit 206 can determine the portion of the reference image corresponding to the exclusion area by further aligning the mask image with the reference image and the image of the printed material to be inspected. As described above, since the mask image is generated for the reference image and other printed images that have been aligned with each other, the reference image and the mask image are aligned with each other. Furthermore, since the alignment of the reference image or the image of the printed material to be inspected with the reference image can be performed in the same way as in S302, it is possible to align the mask image with the reference image or the image of the printed material to be inspected based on this alignment result.

[0037] The inspection unit 206 can then determine as an abnormal pixel any pixel that is not included in the exclusion area and whose pixel value difference between the reference image and the image of the printed material to be inspected is greater than or equal to a predetermined value. In this embodiment, a printed material corresponding to an image without abnormal pixels passes the inspection, while a printed material corresponding to an image with abnormal pixels fails the inspection. However, the method for determining the inspection result is not limited to this method. For example, the inspection unit 206 may determine that a printed material fails the inspection if the ratio of the number of abnormal pixels to the total number of pixels in the image of the printed material to be inspected is greater than or equal to a predetermined value.

[0038] (Method for calculating unbiased variance) Here, as an example of a method for calculating features that show the differences between images in S303, we will explain how to calculate the unbiased variance of the pixel value at each pixel.

[0039] For n print images held in main memory 104, let (Rixy, Gixy, Bixy) be the pixel values ​​of the i-th print image (1≦i≦n) at coordinate (x,y). Also, the average of the pixel values ​​(Rixy, Gixy, Bixy) for the n print images is

number

number

[0040] Alternatively, the unbiased variance S of the brightness values ​​at each coordinate. 2Vxy may be calculated. For n printed images held in main memory 104, the brightness value of the i-th printed image at coordinate (x,y) is Vixy, and the average of the brightness values ​​Vixy for the n printed images is calculated.

number

number

[0041] According to this embodiment, based on the evaluation of differences between multiple printed images by the evaluation unit 203, the setting unit 204 sets exclusion areas for inspection in print inspection, thereby omitting print inspection in areas with large image differences. In other words, print inspection is performed in areas where the variation between multiple printed images is small and print inspection can be performed accurately by comparison with a reference image. On the other hand, in areas where the variation between multiple printed images is large and print inspection by comparison with a reference image is not easy, print inspection can be omitted, thereby suppressing over-detection of abnormalities in the printed material. According to this embodiment, since the image processing device 100 can set such exclusion areas, the user's setting burden can also be reduced.

[0042] As a specific example, when inspecting printed materials on pre-printed paper, the relative position of the pre-printed image with respect to the printed object may fluctuate as described above. In this case, if the reference image and the image of the printed material under inspection are aligned based on the object printed by the printing device 190, the position of the pre-printed image is likely to shift between the reference image and the image of the printed material under inspection. However, the pre-printed image is not an image printed by the printing device 190, and the need to inspect the printed material against the pre-printed image is not very high.

[0043] In this embodiment, the image acquisition unit 201 can acquire multiple printed images by reading each of multiple printed materials on which a common object is printed on a medium that has an image pre-printed on it. The evaluation unit 203 can then detect regions where the position of the common object varies among the multiple printed images by evaluating the differences between the multiple printed images as described above. The regions detected in this way are regions of pre-printed images, i.e., pre-print regions. By detecting the position of the pre-print image in this way and omitting print inspection of the pre-print image, it is possible to suppress the over-detection of abnormalities in the printed material within the pre-print image region.

[0044] (Other methods for evaluating differences between images) The method for evaluating differences between multiple printed images is not limited to the method described above. For example, differences between multiple printed images may be evaluated based on a composite image of the multiple printed images, or based on a comparison between this composite image and the printed images. For example, the degree of blur in the composite image at each position can be used to evaluate differences between multiple printed images. The following describes a case in which the evaluation unit 203 evaluates the amount of blur at each position in the composite image of the multiple printed images in order to evaluate the differences.

[0045] In this case, in S302, the alignment unit 202 aligns the other printed images with respect to the reference image and combines the aligned reference image and the other printed images. Specifically, the alignment unit 202 can generate a composite image having the average value of the pixel values ​​of each pixel. The generated composite image can be stored in the main memory 104. Note that the composite image of multiple printed images may be obtained by combining multiple printed images excluding the reference image.

[0046] Furthermore, in S303, the evaluation unit 203 calculates feature quantities indicating the degree of blurring of the composite image by comparing the composite image held in the main memory 104 with the reference image. The method for calculating the feature quantities is not particularly limited, but examples include the following methods. Note that feature quantities indicating the degree of blurring of the composite image may also be calculated without using the reference image by processing the composite image, such as processing to calculate the edge quantity.

[0047] (A) Calculation of features based on the difference in pixel values The evaluation unit 203 can determine the difference in color information at a corresponding position between a composite image of multiple printed images and one of the multiple printed images, as a feature quantity indicating the magnitude of the difference. For example, the evaluation unit 203 can calculate the difference (Rd, Gd, Bd) between the pixel values ​​(Rr, Gr, Br) of the composite image and the pixel values ​​(Rm, Gm, Bm) of the reference image at coordinate (x, y). The maximum value of the difference Rd, Gd, Bd for each color (R, G, B) can then be used as a feature quantity. A large feature quantity at a particular pixel indicates a large difference in pixel values ​​between the reference image and the other printed images at that pixel, that is, a large degree of blurring in the composite image at that pixel.

[0048] (B) Calculation of features based on edge size The evaluation unit 203 can determine the difference in edge size at corresponding positions between a composite image of multiple printed images and one of the multiple printed images, as a feature quantity indicating the magnitude of the difference. For example, the evaluation unit 203 can generate an edge image showing the size of the edge at each pixel by performing edge detection processing on both the composite image and the reference image to be compared. The evaluation unit 203 can generate a composite edge image 701 and a reference edge image 702 from the composite image and the reference image, respectively, by performing grayscale processing and edge detection processing using the Canny algorithm, for example. The composite edge image 701 and the reference edge image 702 are binarized images in which the edge portions are emphasized, showing areas with large and small edge sizes. The evaluation unit 203 then calculates the difference between the brightness value Vr of the composite edge image and the brightness value Vm of the reference edge image for each pixel, and the calculated difference can be used as a feature quantity for each pixel. A large value of this feature in a particular pixel indicates that the edge position of the reference image and the edge positions of other printed images are misaligned around that pixel, meaning that the composite image is significantly blurred at that pixel.

[0049] (C) Create a histogram and compare contrast and distortion. The evaluation unit 203 can determine the difference in pixel value histograms at corresponding positions between a composite image of multiple printed images and one of the multiple printed images, as a feature quantity indicating the magnitude of the difference. For example, the evaluation unit 203 can create histograms of the composite image and the reference image to be compared, separated by RGB values. These histograms can be created for each region at corresponding positions between the composite image and the reference image. For example, the composite image and the reference image can each be divided into equal-sized sections, and a histogram can be created for each of the divided regions at corresponding positions. Figures 6(A) and (B) show examples of R value histograms for the composite image and the reference image, respectively. In Figures 6(A) and (B), the horizontal axis of the histogram represents pixel values, and the vertical axis represents the number of pixels. The evaluation unit 203 can calculate the difference d between the number of pixels Nr in the histogram of the composite image and the number of pixels Nm in the histogram of the reference image as a feature quantity for each RGB value and each pixel value v. The evaluation unit 203 can use the sum of the absolute values ​​of the differences d obtained for each RGB value and each pixel value v as a feature quantity for each region. A large feature quantity in a particular region indicates that the difference between the reference image and other printed images is large in that region, that is, the degree of blurring in the composite image is large in that region.

[0050] In composite images of multiple printed images, blurred areas suggest significant image variation between individual printed images. By omitting print inspection in such areas, over-detection of print defects can be suppressed, similar to the embodiment described above.

[0051] In the above embodiment, the alignment unit 202 aligned the reference image and the printed image. However, if the variation in the position of the image printed on the printed material is sufficiently small, and the variation in the position of the printed material in the scanned printed image is sufficiently small, alignment may not be necessary. In this case, the evaluation unit 203 can evaluate the differences between multiple printed images at the same coordinates.

[0052] The above embodiments mainly described the inspection of printed materials obtained by printing an object on preprinted paper. However, the target of print inspection in each embodiment is not limited to printed materials on preprinted paper. For example, each of multiple printed materials may have a common area where an image common to all printed materials is printed by the printing device 190, and a variable area where a different image is printed for each printed material by the printing device 190. Such a variable area is a part in which image variations are easily detected. In this case, the evaluation unit 203 can detect areas where differences exist, i.e., variable areas, by similarly evaluating the differences between multiple printed images. In this case, the setting unit 204 can set the variable area as an exclusion area. With such a configuration, it becomes possible to perform inspection of the common area of ​​multiple printed materials using a common reference image.

[0053] (Other examples) The present invention can also be realized by supplying a program that implements one or more of the functions of the above-described embodiments to a system or device via a network or storage medium, and by having one or more processors in the computer of that system or device read and execute the program. It can also be realized by a circuit (e.g., an ASIC) that implements one or more functions.

[0054] The disclosures herein include the following image processing apparatus, image processing methods, and programs.

[0055] (Item 1) An acquisition means for obtaining multiple printed images obtained by reading each of multiple printed materials, An evaluation means for evaluating the differences between the plurality of printed images, Based on the evaluation results by the evaluation means, a setting means for setting exclusion areas in the print inspection, An image processing apparatus characterized by comprising:

[0056] (Item 2) Each of the aforementioned printed materials contains a common object, The image processing apparatus according to item 1, characterized in that the evaluation means evaluates the differences in the relative positions of the objects among the plurality of printed images.

[0057] (Item 3) Each of the aforementioned printed materials contains a common object, The image processing apparatus according to item 1, characterized in that the evaluation means aligns the plurality of printed images based on the object and evaluates the differences between the plurality of printed images after alignment.

[0058] (Item 4) The image processing apparatus according to any one of items 1 to 3, characterized in that the evaluation means evaluates local differences between the plurality of printed images at each position.

[0059] (Item 5) The image processing apparatus according to any one of items 1 to 4, characterized in that the setting means sets an area where the magnitude of the difference is greater than a predetermined standard as the exclusion area.

[0060] (Item 6) The image processing apparatus according to any one of items 1 to 5, characterized in that each of the plurality of printed materials is a printed material on which the object is printed on a medium on which an image has been printed beforehand.

[0061] (Item 7) The image processing apparatus according to any one of items 1 to 6, characterized in that the setting means sets an area where the magnitude of the difference is greater than a predetermined standard as the exclusion area.

[0062] (Item 8) The image processing apparatus according to any one of items 1 to 7, wherein the evaluation means evaluates the difference in color information at corresponding positions between the plurality of printed images in order to evaluate the difference.

[0063] (Item 9) The image processing apparatus according to any one of items 1 to 8, wherein the evaluation means is characterized by determining the variance of color information at corresponding positions between the plurality of printed images as a feature quantity indicating the magnitude of the difference.

[0064] (Item 10) The image processing apparatus according to any one of items 1 to 7, characterized in that the evaluation means evaluates the amount of blur at each position of the composite image of the plurality of printed images in order to evaluate the difference.

[0065] (Item 11) The image processing apparatus according to any one of items 1 to 7 and 10, wherein the evaluation means is characterized by determining the difference in color information at a corresponding position between the composite image of the plurality of printed images and one of the plurality of printed images, as a feature quantity indicating the magnitude of the difference.

[0066] (Item 12) The image processing apparatus according to any one of items 1 to 7 and 10, wherein the evaluation means is characterized by determining the difference in edge quantity at a corresponding position between the composite image of the plurality of printed images and one of the plurality of printed images as a feature quantity indicating the magnitude of the difference.

[0067] (Item 13) The image processing apparatus according to any one of items 1 to 7 and 10, wherein the evaluation means is characterized by determining the difference in the pixel value histogram at a corresponding position between the composite image of the plurality of printed images and one of the plurality of printed images, as a feature quantity indicating the magnitude of the difference.

[0068] (Item 14) The image processing apparatus according to any one of items 1 to 13, further comprising a display control means for displaying the set exclusion area on a display.

[0069] (Item 15) The image processing apparatus according to any one of items 1 to 14, further comprising inspection means for performing an inspection on a printed material to be inspected in such a way as not to perform an inspection on the excluded area.

[0070] (Item 16) The image processing apparatus according to item 15, wherein the inspection means performs an inspection of the printed material based on a comparison at corresponding positions between an image obtained by reading the printed material to be inspected and a reference image showing an acceptable print result. (Item 17) The image processing apparatus according to item 15 or 16, characterized in that the acquisition means acquires an image obtained by reading the printed material to be inspected as at least one of the plurality of printed images.

[0071] (Item 18) An acquisition means for acquiring multiple printed images obtained by reading each of multiple printed materials that have a common object printed on them, on a medium that already has an image printed on it, A detection means for detecting regions where the position of the common object varies among the multiple printed images as regions of the pre-printed image, An image processing apparatus characterized by comprising:

[0072] (Item 19) An image processing method performed by an image processing device, The acquisition process involves obtaining multiple printed images by reading each of multiple printed materials, An evaluation step for evaluating the differences between the plurality of printed images, Based on the results of the evaluation by the evaluation means, a setting step is made to set an exclusion area for inspection in the printed material inspection, An image processing method characterized by including

[0073] (Item 20) A program to cause a computer to function as an image processing device as described in any one of items 1 through 18.

[0074] The invention is not limited to the embodiments described above, and various modifications and variations are possible without departing from the spirit and scope of the invention. Accordingly, claims are attached to disclose the scope of the invention. [Explanation of Symbols]

[0075] 100: Image processing device, 190: Printing device, 201: Image acquisition unit, 202: Alignment unit, 203: Evaluation unit, 204: Setting unit, 205: Display control unit, 206: Inspection unit

Claims

1. Acquisition means for acquiring multiple printed images obtained by reading each of multiple printed materials, An evaluation means for evaluating the differences between the plurality of printed images, Based on the evaluation results by the evaluation means, a setting means for setting exclusion areas in the print inspection, Equipped with, The evaluation means is characterized by determining the variance of color information at corresponding positions between the plurality of printed images as a feature quantity indicating the magnitude of the difference.

2. Acquisition means for acquiring multiple printed images obtained by reading each of multiple printed materials, An evaluation means for evaluating the differences between the plurality of printed images, Based on the evaluation results by the evaluation means, a setting means for setting exclusion areas in the print inspection, Equipped with, The evaluation means is an image processing apparatus characterized by evaluating the amount of blur at each position of the composite image of the plurality of printed images in order to evaluate the difference.

3. Acquisition means for acquiring multiple printed images obtained by reading each of multiple printed materials, An evaluation means for evaluating the differences between the plurality of printed images, Based on the evaluation results by the evaluation means, a setting means for setting exclusion areas in the print inspection, Equipped with, The evaluation means is an image processing apparatus characterized by determining the difference in color information at a corresponding position between a composite image of the plurality of printed images and one of the plurality of printed images, as a feature quantity indicating the magnitude of the difference.

4. Acquisition means for acquiring multiple printed images obtained by reading each of multiple printed materials, An evaluation means for evaluating the differences between the plurality of printed images, Based on the evaluation results by the evaluation means, a setting means for setting exclusion areas in the print inspection, Equipped with, The evaluation means is an image processing apparatus characterized by determining the difference in edge amounts at corresponding positions between a composite image of the plurality of printed images and one of the plurality of printed images, as a feature quantity indicating the magnitude of the difference.

5. Acquisition means for acquiring multiple printed images obtained by reading each of multiple printed materials, An evaluation means for evaluating the differences between the plurality of printed images, Based on the evaluation results by the evaluation means, a setting means for setting exclusion areas in the print inspection, Equipped with, The evaluation means is an image processing apparatus characterized by determining the difference in the pixel value histogram at a corresponding position between a composite image of the plurality of printed images and one of the plurality of printed images, as a feature quantity indicating the magnitude of the difference.

6. Each of the aforementioned printed materials contains a common object, The image processing apparatus according to claim 1, characterized in that the evaluation means evaluates the differences in locations where the relative position to the object is the same among the plurality of printed images.

7. Each of the aforementioned printed materials contains a common object, The image processing apparatus according to claim 1, characterized in that the evaluation means aligns the plurality of printed images based on the object and evaluates the differences between the plurality of printed images after alignment.

8. The image processing apparatus according to claim 7, characterized in that the evaluation means evaluates local differences between the plurality of printed images at each position.

9. The image processing apparatus according to claim 8, characterized in that the setting means sets an area where the magnitude of the difference is greater than a predetermined standard as the exclusion area.

10. The image processing apparatus according to claim 9, characterized in that each of the plurality of printed materials is a printed material on which the object is printed on a medium on which an image has been printed beforehand.

11. The image processing apparatus according to claim 1, characterized in that the setting means sets an area where the magnitude of the difference is greater than a predetermined standard as the exclusion area.

12. The image processing apparatus according to claim 1, further comprising display control means for displaying the set exclusion area on a display.

13. The image processing apparatus according to claim 1, further comprising inspection means for performing an inspection on a printed material to be inspected in such a way as not to perform an inspection on the excluded area.

14. The image processing apparatus according to claim 13, characterized in that the inspection means performs an inspection of the printed material based on a comparison at corresponding positions between an image obtained by reading the printed material to be inspected and a reference image showing an acceptable print result.

15. The image processing apparatus according to claim 14, characterized in that the acquisition means acquires an image obtained by reading the printed material to be inspected as at least one of the plurality of printed images.

16. An image processing method performed by an image processing device, The acquisition process involves obtaining multiple printed images by reading each of multiple printed materials, An evaluation step for evaluating the differences between the plurality of printed images, Based on the evaluation results in the aforementioned evaluation step, a setting step is performed to set exclusion areas for inspection in the printed material inspection, Includes, The image processing method is characterized in that, in the evaluation step, the variance of color information at corresponding positions between the plurality of printed images is determined as a feature quantity indicating the magnitude of the difference.

17. An image processing method performed by an image processing device, The acquisition process involves obtaining multiple printed images by reading each of multiple printed materials, An evaluation step for evaluating the differences between the plurality of printed images, Based on the evaluation results in the aforementioned evaluation step, a setting step is performed to set exclusion areas for inspection in the printed material inspection, Includes, The image processing method is characterized in that, in the evaluation step, the amount of blur at each position of the composite image of the plurality of printed images is evaluated in order to evaluate the difference.

18. An image processing method performed by an image processing device, The acquisition process involves obtaining multiple printed images by reading each of multiple printed materials, An evaluation step for evaluating the differences between the plurality of printed images, Based on the evaluation results in the aforementioned evaluation step, a setting step is performed to set exclusion areas for inspection in the printed material inspection, Includes, The image processing method is characterized in that, in the evaluation step, the difference in color information at a corresponding position between the composite image of the multiple printed images and one of the multiple printed images is determined as a feature quantity indicating the magnitude of the difference.

19. An image processing method performed by an image processing device, The acquisition process involves obtaining multiple printed images by reading each of multiple printed materials, An evaluation step for evaluating the differences between the plurality of printed images, Based on the evaluation results in the aforementioned evaluation step, a setting step is performed to set exclusion areas for inspection in the printed material inspection, Includes, The image processing method is characterized in that, in the evaluation step, the difference in edge quantity at a corresponding position between the composite image of the multiple printed images and one of the multiple printed images is determined as a feature quantity indicating the magnitude of the difference.

20. An image processing method performed by an image processing device, The acquisition process involves obtaining multiple printed images by reading each of multiple printed materials, An evaluation step for evaluating the differences between the plurality of printed images, Based on the evaluation results in the aforementioned evaluation step, a setting step is performed to set exclusion areas for inspection in the printed material inspection, Includes, The image processing method is characterized in that, in the evaluation step, the difference in the pixel value histogram at a corresponding position between the composite image of the multiple printed images and one of the multiple printed images is determined as a feature quantity indicating the magnitude of the difference.

21. A program for causing a computer to function as an image processing device according to any one of claims 1 to 15.

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