IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND IMAGE PROCESSING PROGRAM

Abstract

To provide an image processing apparatus configured to exclude a false anomaly from detected anomalies to improve the accuracy of detecting anomalies, an image processing method, and an image processing program.SOLUTION: An anomaly detection unit 12 detects anomalies included in a target image. An anomaly exclusion processing unit 13 excludes a specific anomaly among the detected anomalies. Further, the anomaly exclusion processing unit 13 excludes one of an anomaly and another anomaly among the detected anomalies, if (a) a detection area of the anomaly, a detection area of the other anomaly, and an overlapping area of the detection areas of the anomaly and the other anomaly satisfy a predetermined condition and (b) a type of the anomaly and a type of the other anomaly are different from each other.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an image processing device, an image processing method, and an image processing program. [Background technology]

[0002] One image processing device detects abnormalities such as unintended streaks, dots, and unevenness that appear on print products output by image forming devices such as multifunction devices and printers based on images obtained by scanning the print products, and estimates the cause of each abnormality (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-223892 A Summary of the Invention [Problem to be solved by the invention]

[0004] Generally, when trying to make it easier to detect anomalies, overdetection can occur, and images that are not abnormal can be detected as abnormal. Although the image processing device described above can detect anomalies, the detection results may include overdetected anomalies (false anomalies). In addition, subsequent processing such as estimating the cause of occurrence is performed on such false anomalies, which results in extra calculation costs.

[0005] The present invention has been made in consideration of the above problems, and aims to provide an image processing device, an image processing method, and an image processing program that can eliminate false anomalies from detected anomalies, thereby improving the accuracy of anomaly detection. [Means for solving the problem]

[0006] The image processing device according to the present invention includes an anomaly detection unit that detects anomalies contained in a target image, and an anomaly exclusion processing unit that excludes a specific anomaly from the detected anomalies. (a) an area ratio between an overlapping area between a detection area of ​​one of the detected anomalies and a detection area of ​​another anomaly and an area ratio between the detection area of ​​the one anomaly and the detection area of ​​the other anomaly satisfy a predetermined condition; and (b) when the type of the one anomaly and the type of the other anomaly are different from each other, one of the one anomaly and the other anomaly is excluded from the detected anomalies.

[0007] The image processing method according to the present invention comprises: On your computer, an anomaly detection step for detecting an anomaly contained in the target image; In the computer, and an anomaly excluding step of excluding a specific anomaly from the detected anomalies. (a) an area ratio between an overlapping area between a detection area of ​​one of the detected anomalies and a detection area of ​​another anomaly and an area ratio between the detection area of ​​the one anomaly and the detection area of ​​the other anomaly satisfy a predetermined condition; and (b) when the type of the one anomaly and the type of the other anomaly are different from each other, one of the one anomaly and the other anomaly is excluded from the detected anomalies.

[0008] An image processing program according to the present invention causes a computer to function as the abnormality detection unit and the abnormality exclusion processing unit described above. Effect of the Invention

[0009] According to the present invention, an image processing device, an image processing method, and an image processing program are provided that can improve the accuracy of anomaly detection by eliminating false anomalies from detected anomalies.

[0010] The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing a configuration of an image processing device according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a flowchart illustrating the operation of the image processing device shown in FIG. [Diagram 3]FIG. 3 is a flowchart illustrating the abnormality exclusion process (step S3) in FIG. [Figure 4] FIG. 4 is a diagram illustrating an example of the abnormality exclusion process (step S3) in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0013] Fig. 1 is a block diagram showing the configuration of an image processing device according to an embodiment of the present invention. The image processing device shown in Fig. 1 is an information processing device such as a personal computer or a server, or an electronic device such as a digital camera or an image forming device (scanner, multifunction machine, etc.), and includes an arithmetic processing device 1, a storage device 2, a communication device 3, a display device 4, an input device 5, an internal device 6, etc.

[0014] The arithmetic processing device 1 includes a computer, and executes an image processing program on the computer to operate as various processing units. Specifically, the computer includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like, and operates as a specific processing unit by loading a program stored in the ROM or storage device 2 into the RAM and executing the program on the CPU. The arithmetic processing device 1 may also include an ASIC (Application Specific Integrated Circuit) that functions as a specific processing unit.

[0015] The storage device 2 is a non-volatile storage device such as a flash memory, and stores an image processing program and data necessary for the processing described below. The image processing program is stored in, for example, a non-transitory computer-readable recording medium, and is installed in the storage device 2 from the recording medium.

[0016] The communication device 3 is a device that performs data communication with an external device, such as a network interface, a peripheral device interface, etc. The display device 4 is a device that displays various information to the user, such as a display panel such as a liquid crystal display, etc. The input device 5 is a device that detects user operations, such as a keyboard, a touch panel, etc.

[0017] The internal device 6 is a device that executes a predetermined function of the image processing device. For example, if the image processing device is an image forming device, the internal device 6 is an image reading device that optically reads an original image from an original, a printing device that prints an image on a printing paper, or the like.

[0018] Here, the arithmetic processing device 1 operates as the target image acquisition unit 11, the abnormality detection unit 12, the abnormality exclusion processing unit 13, and the abnormality response processing unit 14, which are the processing units described above.

[0019] The target image acquisition unit 11 acquires a target image (image data) from the storage device 2, the communication device 3, the internal device 6, etc., and stores it in a RAM or the like.

[0020] The anomaly detection unit 12 detects anomalies contained in the acquired target image by using an existing method. In this embodiment, the anomaly detection unit 12 detects anomalies using, for example, a filter (second-order differential filter, Gabor filter, etc.) and generates feature information of the anomaly. In other words, anomalies are detected based on the feature amount (filter output) obtained by applying the filter to the input image.

[0021] The feature information includes the position and size of the abnormality (the position and size of the detection area described below), the type of the abnormality (streak, band, point, etc.), and the degree of abnormality corresponding to the output value of the filter when the abnormality is detected. For example, when the density difference between the surroundings of the abnormality and the abnormality is large, the degree of abnormality is large.

[0022] The anomaly exclusion processing unit 13 excludes a specific anomaly from the detected anomalies. Specifically, when (a) an overlapping region between a detection region of a certain anomaly and a detection region of another anomaly among the detected anomalies, and the detection region of the certain anomaly and the detection region of the other anomaly satisfy a predetermined condition, and (b) the type of the certain anomaly and the type of the other anomaly are different from each other, the anomaly exclusion processing unit 13 excludes one of the certain anomaly and the other anomaly from the detected anomalies.

[0023] The detection area is a rectangular area that includes an abnormality, and is specified by the abnormality detection unit 12.

[0024] In this embodiment, when the type of the certain anomaly and the type of the other anomaly are different from each other, the anomaly exclusion processing unit 13 selects one of the certain anomaly and the other anomaly based on the degree of anomaly and excludes the selected anomaly from the detected anomalies. Specifically, the anomaly with the lower degree of anomaly is excluded.

[0025] Furthermore, in this embodiment, the anomaly exclusion processing unit 13 (a) derives an area A (specifically, the product of the number of pixels in the width and the number of pixels in the height) of the detection area of ​​the above-mentioned certain anomaly, an area B of the detection area of ​​the above-mentioned other anomaly, and an area C of the overlapping area between the detection area of ​​the above-mentioned certain anomaly and the detection area of ​​the above-mentioned other anomaly among the detected anomalies, and (b) if the area ratios R1, R2 (R1=C / A, R2=C / B) satisfy a predetermined condition (here, R1 and R2 are greater than or equal to a predetermined threshold value TH) and (c) the type of the above-mentioned certain anomaly and the type of the above-mentioned other anomaly are different from each other, excludes one of the above-mentioned certain anomaly and the above-mentioned other anomaly from the detected anomalies.

[0026] For example, the certain abnormality type and the other abnormality type are muscle abnormality and band abnormality. That is, for example, among the detected abnormalities, the abnormality exclusion process is performed on muscle abnormality and band abnormality that overlap each other.

[0027] The abnormality handling processing unit 14 executes a predetermined abnormality handling process for the detected abnormality (other than the excluded abnormality). In other words, the abnormality handling process is not executed for the excluded abnormality. The abnormality handling process includes reporting each detected abnormality (reporting by sending a message via the communication device or displaying a message on the display device 4 to an operator who identifies the defective part corresponding to the abnormality or performs maintenance, etc.), identifying the defective part corresponding to each detected abnormality, performing maintenance operations, etc.

[0028] Next, a description will be given of the operation of the image processing device shown in Fig. 1. Fig. 2 is a flow chart illustrating the operation of the image processing device shown in Fig. 1.

[0029] First, the target image acquisition unit 11 acquires a target image (image data) (step S1). Next, the anomaly detection unit 12 detects anomalies contained in the acquired target image, generates characteristic information of the detected anomalies (position and size information, type, degree of anomaly, etc.), and stores it in a RAM or the like (step S2).

[0030] Next, the abnormality exclusion processing unit 13 executes an abnormality exclusion process for excluding a specific abnormality from the detected abnormalities (step S3).

[0031] The abnormality handling processor 14 executes a predetermined abnormality handling process for the detected abnormality (other than the excluded abnormality) (step S4).

[0032] Here, the abnormality exclusion process in step S3 will be described. Fig. 3 is a flowchart illustrating the abnormality exclusion process (step S3) in Fig. 2. Fig. 4 is a diagram illustrating an example of the abnormality exclusion process (step S3) in Fig. 2.

[0033] First, the anomaly exclusion processor 13 determines whether or not there is an overlap of anomalies (detection regions) among the detected anomalies (step S11). Specifically, the anomaly exclusion processor 13 selects the detected anomalies in order, and determines whether or not a part or all of the detection region of the selected anomaly overlaps with the detection region of any other anomaly based on the above-mentioned characteristic information, thereby detecting a pair of two overlapping anomalies (detection regions).

[0034] Then, when a pair of two overlapping abnormalities (detection regions) is detected, the abnormality exclusion processing unit 13 determines whether or not the types of the two overlapping abnormalities are different (step S12).

[0035] If it is determined that the types of the two overlapping anomalies are different, the anomaly exclusion processing unit 13 calculates the above-mentioned area ratios R1 and R2 for the detection areas of the two overlapping anomalies, and determines whether or not both of the above-mentioned area ratios R1 and R2 are equal to or greater than the threshold value TH (step S13).

[0036] If it is determined that both of the calculated area ratios R1 and R2 are equal to or greater than the threshold value TH, the anomaly exclusion processing unit 13 identifies the anomaly degrees of those two anomalies based on the above-mentioned characteristic information (step S14), and excludes the anomaly with the lower anomaly degree from among those two anomalies (step S15).

[0037] In this manner, a specific anomaly is excluded from the detected anomalies. If no overlap of different types of anomalies is detected in steps S11 and S12, and if at least one of the area ratios R1 and R2 of the overlap of different types of anomalies is less than the threshold value TH in step S13, the exclusion of the anomaly is not performed.

[0038] For example, as shown in FIG. 4, for overlapping muscle and band abnormalities, if both area ratios R1 and R2 are equal to or greater than the threshold value TH, one of the abnormalities is excluded, whereas if one or both of the area ratios R1 and R2 are less than the threshold value TH, the exclusion of the abnormality is not performed.

[0039] As described above, according to the above embodiment, anomaly detection unit 12 detects anomalies contained in a target image. Anomaly exclusion processing unit 13 excludes a specific anomaly from the detected anomalies. Then, when (a) an overlapping region between a detection region of a certain anomaly and a detection region of another anomaly among the detected anomalies, and the detection region of the certain anomaly and the detection region of the other anomaly satisfy a predetermined condition, and (b) the type of the certain anomaly and the type of the other anomaly are different from each other, anomaly exclusion processing unit 13 excludes either the certain anomaly or the other anomaly from the detected anomalies.

[0040] This allows false anomalies to be excluded from the detected anomalies, improving the accuracy of anomaly detection, and ultimately reducing the calculation costs and processing time required for anomaly response processing.

[0041] It should be noted that various changes and modifications to the above-described embodiments will be apparent to those skilled in the art. Such changes and modifications may be made without departing from the spirit and scope of the subject matter and without diminishing its intended advantages. In other words, such changes and modifications are intended to be included within the scope of the claims.

[0042] For example, in the above embodiment, the above threshold value may be a value according to the type of abnormality.

[0043] In addition, in the above embodiment, the above-mentioned specified condition is that the area ratios R1, R2 are greater than or equal to a threshold value, but instead, another condition (for example, that the ratio between the area C of the overlapping area and the area D of the union of both detection areas is greater than or equal to a threshold value) may be used. [Industrial Applicability]

[0044] The present invention is applicable, for example, to anomaly detection. [Explanation of symbols]

[0045] 1. Processing unit (an example of a computer) 12 Anomaly detection section 13 Abnormality exclusion processing unit

Claims

1. an anomaly detection unit that detects anomalies contained in a target image; an anomaly exclusion processing unit that excludes a specific anomaly from the detected anomalies, the anomaly exclusion processing unit excludes one of the one anomaly and the other anomaly from the detected anomalies when (a) an area ratio between an overlapping area between a detection area of ​​a certain anomaly and a detection area of ​​another anomaly among the detected anomalies and a detection area of ​​the certain anomaly and the detection area of ​​the other anomaly satisfies a predetermined condition, and (b) a type of the certain anomaly and a type of the other anomaly are different from each other; An image processing device comprising:

2. the anomaly detection unit detects the anomaly using a filter and generates characteristic information of the anomaly; the characteristic information includes a type of the abnormality and an abnormality degree corresponding to an output value of the filter at the time when the abnormality is detected, the anomaly exclusion processing unit, when a type of the certain anomaly and a type of the other anomaly are different from each other, selects one of the certain anomaly and the other anomaly based on the degree of anomaly, and excludes the selected anomaly from the detected anomalies; 2. The image processing device according to claim 1,

3. 3. The image processing apparatus according to claim 1, wherein the one abnormality type and the another abnormality type are a muscle abnormality and a girdle abnormality.

4. An anomaly detection step of detecting anomalies contained in a target image by a computer; and an anomaly exclusion step of excluding, by the computer, a specific anomaly from the detected anomalies, In the anomaly exclusion step, (a) when an area ratio between an overlapping area between a detection area of ​​a certain anomaly and a detection area of ​​another anomaly among the detected anomalies and an area ratio between the detection area of ​​the certain anomaly and the detection area of ​​the other anomaly satisfy a predetermined condition, and (b) when a type of the certain anomaly and a type of the other anomaly are different from each other, one of the certain anomaly and the other anomaly is excluded from the detected anomalies; An image processing method comprising:

5. causing the computer to function as an anomaly detection unit that detects anomalies contained in the target image and an anomaly exclusion processing unit that excludes specific anomalies from the detected anomalies; the anomaly exclusion processing unit excludes one of the one anomaly and the other anomaly from the detected anomalies when (a) an area ratio between an overlapping area between a detection area of ​​a certain anomaly and a detection area of ​​another anomaly among the detected anomalies and a detection area of ​​the certain anomaly and the detection area of ​​the other anomaly satisfies a predetermined condition, and (b) a type of the certain anomaly and a type of the other anomaly are different from each other; An image processing program characterized by:

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