WO2023068323A1 - 画像処理装置、画像処理方法及びプログラム - Google Patents

画像処理装置、画像処理方法及びプログラム Download PDF

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Publication number
WO2023068323A1
WO2023068323A1 PCT/JP2022/039075 JP2022039075W WO2023068323A1 WO 2023068323 A1 WO2023068323 A1 WO 2023068323A1 JP 2022039075 W JP2022039075 W JP 2022039075W WO 2023068323 A1 WO2023068323 A1 WO 2023068323A1
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Prior art keywords
folder
image
resolution
images
management means
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PCT/JP2022/039075
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English (en)
French (fr)
Japanese (ja)
Inventor
智大 佐藤
淳一 児玉
勇樹 釜森
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Canon Inc
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Canon Inc
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Publication of WO2023068323A1 publication Critical patent/WO2023068323A1/ja
Priority to US18/624,185 priority Critical patent/US20240242333A1/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/0008—Industrial image inspection checking presence/absence

Definitions

  • the present invention relates to an image processing technology for detecting deformation from images of an inspection target.
  • a computer machine performs machine learning on images of inspection objects such as the walls of concrete structures to detect deformations such as cracks, and to detect deformation attributes such as crack widths through image analysis. There is a way to do
  • a user terminal transmits a plurality of images of an inspection target to a server device, and the server device performs image analysis on a composite image obtained by synthesizing the plurality of images to detect deformation. system is described.
  • the analysis results (cracks) can be corrected, and attribute information (crack width) can be added to the analysis results (cracks), and this information is associated and stored in the database. be done.
  • a scale image showing the crack and the actual size can be superimposed and displayed on the user terminal.
  • the image of the deformation does not contain the actual size information of the deformation, and in order to judge it accurately, it is necessary to accurately specify the information such as the image resolution that represents the actual size value per pixel of the image.
  • the image resolution that represents the actual size value per pixel of the image.
  • the present invention has been made in view of the above problems, and reduces the burden of operations for setting information such as resolution for images to be detected.
  • the image processing apparatus of the present invention includes folder management means for creating and setting a folder for managing images for detecting deformation of an inspection object, and setting of images to be managed in the folder. wherein the folder management means sets the resolution of the images managed by the folder, and the image management means manages the resolution set by the folder management means by the folder. image.
  • FIG. 1 is a block diagram showing the hardware configuration of an image processing apparatus according to a first embodiment
  • FIG. 2 is a functional block diagram of the image processing apparatus according to the first embodiment
  • FIG. 3 is a diagram exemplifying an image list screen according to the first embodiment
  • FIG. 4 is a diagram exemplifying a folder creation screen according to the first embodiment
  • FIG. 5 is a diagram exemplifying an analysis result list screen according to the first embodiment
  • FIG. 6 is a diagram exemplifying an analysis result viewing screen according to the first embodiment
  • FIG. 1 is a block diagram showing the hardware configuration of an image processing apparatus according to a first embodiment
  • FIG. 2 is a functional block diagram of the image processing apparatus according to the first embodiment
  • FIG. 3 is a diagram exemplifying an image list screen according to the first embodiment
  • FIG. 4 is a diagram exemplifying a folder creation screen according to the first embodiment
  • FIG. 5 is a diagram exemplifying an analysis result list screen according to the
  • FIG. 7A is a diagram for explaining a method of calculating actual size information according to the first embodiment
  • FIG. 7B is a diagram for explaining a method of calculating actual size information according to the first embodiment
  • FIG. 8 is a flowchart showing processing for executing image analysis according to the first embodiment
  • FIG. 9 is a diagram exemplifying an image analysis method selection screen according to the third embodiment
  • FIG. 10 is a diagram exemplifying a folder structure according to the fourth embodiment
  • FIG. 11 is a diagram exemplifying a subfolder creation screen according to the fourth embodiment
  • 12 is a diagram exemplifying a folder editing screen according to the fourth embodiment
  • Deformation refers to cracks on the concrete surface due to damage, deterioration, and other factors in concrete structures such as expressways, bridges, tunnels, dams, etc. Cracks refer to aged deterioration, earthquake impact, etc. It is a linear damage with a start point, end point, length and width that occurs on the wall of a structure due to
  • FIG. 1 is a block diagram showing the hardware configuration of the image processing apparatus 100 of Embodiment 1.
  • FIG. 1 is a block diagram showing the hardware configuration of the image processing apparatus 100 of Embodiment 1.
  • a computer device operates as the image processing device 100 .
  • the processing of the image processing apparatus of this embodiment may be implemented by a single computer device, or may be implemented by distributing each function to a plurality of computer devices as necessary.
  • a plurality of computer devices are communicatively connected to each other.
  • the image processing apparatus 100 includes a control unit 101, a nonvolatile memory 102, a work memory 103, a storage device 104, an input device 105, an output device 106, a network interface 107, and a system bus 108.
  • the control unit 101 includes arithmetic processing processors such as a CPU and an MPU that control the entire image processing apparatus 100 .
  • the nonvolatile memory 102 is a ROM that stores programs executed by the processor of the control unit 101 and parameters.
  • the program is a program for executing the processes of Embodiments 1 and 2, which will be described later.
  • a work memory 103 is a RAM that temporarily stores programs and data supplied from an external device or the like.
  • the storage device 104 is an internal device such as a hard disk or memory card built into the image processing apparatus 100 or an external device such as a hard disk or memory card detachably connected to the image processing apparatus 100 .
  • the storage device 104 includes a memory card, a hard disk, and the like, which are composed of a semiconductor memory, a magnetic disk, and the like. Also, the storage device 104 includes a storage medium configured by a disk drive that reads/writes data from/to optical disks such as DVDs and Blue-ray Discs.
  • the input device 105 is an operation member such as a mouse, keyboard, touch panel, etc. that receives user operations, and outputs operation instructions to the control unit 101 .
  • the output device 106 is a display device such as an LCD or an organic EL display or monitor, and displays data held by the image processing apparatus 100 or data supplied from an external device.
  • the network interface 107 is communicably connected to a network such as the Internet or a LAN (Local Area Network).
  • a system bus 108 includes an address bus, a data bus, and a control bus that connect the components 101 to 107 of the image processing apparatus 100 so that data can be exchanged.
  • the non-volatile memory 102 stores an OS (operating system), which is basic software executed by the control unit 101, and applications that cooperate with the OS to realize applied functions.
  • the nonvolatile memory 102 stores an application for realizing an image analysis process for detecting a deformation from an image of an inspection target, which will be described later, captured by the image processing apparatus 100 .
  • the processing of the image processing apparatus 100 of this embodiment is implemented by reading software provided by an application. It is assumed that the application has software for using the basic functions of the OS installed in the image processing apparatus 100 . Note that the OS of the image processing apparatus 100 may have software for realizing the processing in this embodiment.
  • FIG. 2 is a functional block diagram of the image processing device 200 of the first embodiment.
  • the image processing apparatus 200 has a folder management unit 211 , a folder setting storage unit 212 , an image management unit 213 , an image storage unit 214 , an image analysis unit 215 , an analysis result management unit 216 and an analysis result storage unit 217 .
  • Each function of the image processing apparatus 200 is configured by hardware and software. It should be noted that each functional unit may be configured as a system in which one or a plurality of computer devices or server devices are configured and connected via a network.
  • the folder management unit 211 has a function of at least one of creating, setting, deleting, and displaying a list of folders.
  • the folder setting saving unit 212 has a function of saving folder settings.
  • the folder management unit 211 presents the user with an image list screen, which will be described later with reference to FIG. 3, a folder creation screen, which will be described later with reference to FIG. 4, and an image analysis method selection screen, which will be described later with reference to FIG.
  • the user inputs folder settings such as a folder name and image resolution on the folder creation screen.
  • the folder settings include the folder name and image resolution, but may also include information such as execution memos entered when the folder was created, creation date and time for folder management, and access date and time.
  • Image resolution is the actual size information per pixel of an image, more specifically, a conversion value representing the actual size value (for example, mm) per pixel of the image, and the image actual size ratio (mm/ pixels).
  • the actual size information is also called the actual size conversion of the image, the resolution, the actual pixel size value, the actual image size value, etc., in addition to the actual size ratio of the image. Since the image does not contain the actual size information, the method of manually inputting the actual size information by the user, the method of estimating from the size of the deformation whose actual size information of the image is clear, and the like. In this embodiment, as will be described later, a method for manually inputting actual size information by the user will be described.
  • the image management unit 213 has a function of saving images, registering images in a folder, deleting images, displaying and viewing a list, changing image resolution, and changing file names.
  • the image storage unit 214 stores image data and settings.
  • the image management unit 213 manages a plurality of images by registering them in a folder.
  • the user designates one folder in which the image is registered when saving the image.
  • the image management unit 213 performs setting to apply the image resolution set by the user to the images registered in the folder specified by the user.
  • the image storage unit 214 stores settings of images registered in the folder.
  • the image analysis unit 215 performs image analysis using a learning model created by machine learning/deep learning of AI (artificial intelligence) in order to detect deformation from the image of the inspection target managed in the folder. to run.
  • the analysis result storage unit 217 stores the image analysis result.
  • the analysis result management unit 216 has a function of viewing, acquiring, etc. the image analysis results stored in the analysis result storage unit 217 .
  • the analysis result management unit 216 presents to the user an analysis result list screen, which will be described later with reference to FIG. 5, and an analysis result viewing screen, which will be described later with reference to FIG.
  • the image resolution is sufficient to detect deformation, and it is rare that the entire range of the object to be inspected can be captured in a single image.
  • the work of taking a close-up photograph of a part of the target range is repeated while gradually moving the photographing range. Then, after performing image processing such as enlargement, reduction, rotation, projective transformation, color adjustment, noise removal, etc. on the plurality of images photographed in this way, the plurality of images that have undergone image processing are joined together to form a single image. generates a composite image of
  • This kind of work is repeated according to the number of construction points of the drawing to be inspected. For example, if the cross section of the bridge that constitutes the bridge is a square, it is repeated for the four sides to create four sheets of " ⁇ big bridge pier 1".
  • a standard image resolution for drawings for example, 0.5 mm/pixel for bridges and 2.0 mm/pixel for tunnels
  • high-definition images may be taken. After that, by generating a composite image so as to match the resolution and position of the drawing, preparation of the image for image analysis is completed.
  • deformations detected by image analysis may include false detections and omissions. Therefore, visual confirmation and correction are performed by an image processing device or an external server. For example, if the deformation is a crack, the crack is superimposed on the drawing or image, and the analysis result is created with the length and width of the crack added.
  • FIG. 3 An image list screen and a folder creation screen according to the present embodiment will be described with reference to FIGS. 3 and 4.
  • FIG. 3 An image list screen and a folder creation screen according to the present embodiment will be described with reference to FIGS. 3 and 4.
  • FIG. 3 An image list screen and a folder creation screen according to the present embodiment will be described with reference to FIGS. 3 and 4.
  • FIG. 3 is a diagram illustrating an image list screen.
  • the image list screen 301 shown in FIG. 3 includes a folder list area 302 and an image list area 303 .
  • the folder names of created folders are displayed in a predetermined order (for example, in order of folder name and creation date and time), and a create folder button 312 is displayed.
  • a desired folder is displayed in the folder list 311 .
  • an image list 322 registered in the selected folder is displayed in the image list area 303 .
  • image files registered in a desired folder arbitrarily selected by the user from the folder list 311 displayed in the folder list area 302 are displayed in a predetermined order (for example, in order of file name or creation date/time). ) are displayed side by side.
  • an image list tab 321a In the image list area 303, an image list tab 321a, an analysis result list tab 321b, an image registration button 323, and an analysis start button 324 are displayed.
  • the image list tab 321 a is a button for displaying image files registered in the folder selected from the folder list area 302 in the image list area 303 .
  • the analysis result list tab 321 b is a button for displaying in the image list area 303 the analysis result of the image files registered in the folder selected from the folder list area 302 .
  • the user operates the folder creation button 312 when creating a new folder. Also, the user operates the image registration button 323 when registering an image file in a folder. When executing image analysis, the analysis start button 324 is operated.
  • the user can operate the folder creation button 312 to create a new folder.
  • FIG. 4 is a diagram exemplifying the folder creation screen.
  • the folder creation screen 401 includes a folder name input field 411 , an image resolution input field 412 , an OK button 421 and a cancel button 422 .
  • the user inputs a name such as the name of an inspection target that makes it easy to identify the inspection target in the folder name input field 411, and inputs the image resolution applied to the images registered in the folder in the image resolution input field 412. do.
  • the OK button 421 is a button for confirming the folder name input in the folder name input field 411 and the image resolution input in the image resolution input field 412 and saving the folder settings.
  • a cancel button 422 is a button for canceling the folder name input in the folder name input field 411 and the image resolution input in the image resolution input field 412 and returning to the image list screen 301 .
  • a screen prompting the user to select images to be registered in the folder is displayed.
  • the user can apply the image resolution set to the folder at the time of image registration to all images registered in the folder.
  • the image file displayed in the image list 322 is appended with the image resolution being applied or the image resolution after change.
  • an edit button 325 is displayed for the image file displayed in the image list 322, and the file name and image resolution of the corresponding image file can be edited.
  • a check box 326 is displayed for each image file displayed in the image list 322, and the user selects a desired image file and then operates an analysis start button 324 to accept the input of a memo during execution. Then, image analysis is performed on the checked image files.
  • the execution-time memo is supplementary information that can be arbitrarily input by the user, such as information on the date and time when image analysis was performed, inspection locations, and attention levels of inspection objects.
  • the analysis result list screen 501 shown in FIG. 5 is displayed.
  • image resolution as one of the parameters to the learning model used for image analysis
  • image analysis according to the actual size information of the deformation is executed, and improvement in detection accuracy can be expected compared to the case where the image resolution is not given.
  • FIG. 5 is a diagram illustrating an analysis result list screen.
  • Analysis result list screen 501 includes folder list area 502 and analysis result list area 503 .
  • a folder list area 502 is the same as the folder list area 302 in the image list screen 301 in FIG.
  • a create folder button 512 in the folder list area 502 is the same as the create folder button 312 in the image list screen 302 in FIG.
  • an analysis result list 522, an image list tab 521a, and an analysis result list tab 521b which are results of executing image analysis on image files checked in the image list 322, are displayed.
  • the user can display the image list screen 301 in FIG. 3 by operating the image list tab 521a.
  • analysis result list 522 one of analysis completed, analysis in progress, and analysis failure is displayed as the image analysis status along with an execution memo, and a browse button 523 and a download button 524 are displayed for each analysis result.
  • the browse button 523 is operated for the desired analysis result in the analysis result list 522, the analysis result browse screen 601 shown in FIG. 6 is displayed. data files are downloaded from an external server.
  • FIG. 6 is a diagram illustrating an analysis result browsing screen.
  • a folder name 611a, an execution memo 611b, an analysis result display field 612, a legend display field 613, and a return button 614 are displayed.
  • the folder name 611a and execution memo 611b display the folder name and execution memo in which the image file subjected to the image analysis is registered.
  • an analysis result 621 such as a crack as a deformation detected by image analysis is displayed superimposed on the image to be detected.
  • a crack includes actual size information such as length and thickness (width), and is identifiably displayed in different display forms (for example, color and line type) according to the length and thickness (width) of the crack.
  • the legend display field 613 displays the correspondence between the actual size information of the crack length and thickness (width) and the display mode.
  • the actual size information of the crack length and thickness (width) can be calculated from the image resolution and the number of pixels of the image. Also, by comparing the actual size information of the crack with the data of the drawing, the coordinates of the analysis result can be converted into numerical values matching the coordinate system of the drawing, and can be browsed or edited in an image processing device or an external server.
  • FIGs. 7A and 7B are diagrams explaining a method of calculating the actual size information of a crack from the image resolution and the number of pixels of the image.
  • FIG. 7A is a diagram explaining a calculation method for approximating a crack with a straight line.
  • FIG. 7B is a diagram explaining a calculation method for approximating a crack with a polygonal line.
  • the thickness (width) 711 of the crack is the actual size information to be obtained.
  • the thickness (width) 711 of the crack has an area in the oblique direction, so it is calculated from the horizontal and vertical lengths.
  • the image resolution of the image corresponding to the thickness (width) 711 of the crack is k
  • the number of pixels in the horizontal direction is a
  • the number of pixels in the vertical direction is b
  • the actual size information can be calculated from the following linear approximation formula 1.
  • Equation 1 k ⁇ (a ⁇ a+b ⁇ b) Similarly, for the crack length 712 , in Equation 1, the horizontal pixel number a and the vertical pixel number b corresponding to the crack thickness (width) 711 are replaced by the horizontal pixel number corresponding to the crack length 712 . It can be calculated by replacing the number of pixels c with the number of pixels d in the vertical direction. In the case of a curved crack that is not suitable for straight line approximation, as shown in FIG. can also be calculated.
  • the user can display the analysis result list screen 501 shown in FIG.
  • FIG. 8 is a flowchart showing processing for executing image analysis by the image processing apparatus of this embodiment.
  • control unit 101 of the image processing apparatus 100 shown in FIG. 1 develops a program stored in the nonvolatile memory 102 in the work memory 103 and executes it to control each component. It is realized by executing the function shown.
  • control unit 101 determines whether a folder in which images to be detected are registered has been created. If the control unit 101 determines that the folder has been created, the process advances to S804. If the control unit 101 determines that the folder has not been created, the process advances to step S802.
  • control unit 101 causes the folder management unit 211 to display the image list screen 301 shown in FIG.
  • the control unit 101 causes the folder management unit 211 to display the folder creation screen 401 shown in FIG. Further, when the control unit 101 accepts the operation of the OK button 421 , the folder management unit 211 creates a new folder and saves the folder name and image resolution settings of the newly created folder in the folder setting storage unit 212 . do. By setting the image resolution to be applied to the image in the folder in which the image is registered in this manner, the image resolution set to the folder at the time of image registration can be applied to all the images registered in the folder.
  • control unit 101 causes the folder management unit 211 to display the image list screen 301 shown in FIG.
  • the image management unit 213 sets the image to be registered so that the image resolution in the setting of is applied to the image to be registered, and the image saving unit 214 saves the setting of the image.
  • the image resolution set to the folder at the time of image registration can be applied to all the images registered in the folder.
  • control unit 101 causes the folder management unit 211 to display the image list screen 301 shown in FIG.
  • control unit 101 executes image analysis on the image file checked by the image analysis unit 215.
  • control unit 101 determines whether the image analysis by the image analysis unit 215 has been completed. If the control unit 101 determines that the image analysis is completed, it advances the processing to S808. If the control unit 101 determines that the image analysis has not been completed, the processing returns to S806.
  • control unit 101 causes the analysis result storage unit 217 to store the image analysis result. Further, when receiving an operation on the analysis result list tab 321b on the image list screen 301 shown in FIG. 3, the control unit 101 causes the analysis result management unit 216 to display the analysis result list screen 501 shown in FIG.
  • the image resolution set in the folder at the time of image registration is registered in the folder. Can be applied to all images that are Therefore, since it is not necessary to manually set the image resolution for each image to be detected, it is possible to reduce the burden of the operation for setting the image resolution for the image to be detected. Further, since the image analysis corresponding to the set image resolution is executed, improvement in the accuracy of detecting deformation can be expected compared to the image analysis that does not give the image resolution.
  • a standard value or a recommended value is set in advance through tests, etc., depending on the inspection target.
  • the standard or recommended image resolution for the "piers" of a bridge is "0.5".
  • a warning is displayed because it deviates from the standard value or recommended value.
  • the folder creation screen 401 or OK A warning is displayed to the user on a pop-up screen or the like when the button 421 is operated. This can prompt the user to make corrections.
  • the standard value or recommended value "0.5" may be automatically entered in the image resolution input field 412.
  • the automatically entered values may be changeable by the user.
  • FIG. 9 is a diagram illustrating an image analysis method selection screen.
  • the image analysis method selection screen 901 shown in FIG. 9 is displayed.
  • the image analysis method selection screen 901 includes an execution memo input field 911 , a learning model selection field 912 , a parameter input field 913 , an OK button 921 and a cancel button 922 .
  • a runtime memo similar to that of the first embodiment is entered in the runtime memo input field 911 .
  • a plurality of learning models can be selected in the learning model selection field 912 .
  • the user can select a learning model suitable for the inspection target from a plurality of learning models registered in the learning model selection field 912 .
  • a parameter input field 913 can input parameters such as image resolution to be given to the learning model. Also, for each learning model that can be selected in the learning model selection field 912, multiple (for example, three) parameters can be input.
  • the OK button 921 confirms the execution memo input in the execution memo input field 911, the learning model selected from the learning model selection field 912, and one or more parameters input in the parameter input field 913, and sets the image analysis method.
  • a cancel button 922 is used to cancel the runtime memo input in the runtime memo input field 911, the learning model selected from the learning model selection field 912, and one or more parameters input in the parameter input field 913, and to redo the input. is a button.
  • the learning model is trained in advance using a specific detection target image, it is expected that the user can select a learning model suitable for the inspection target to improve accuracy.
  • improvements can be made by changing parameters in the same learning model and comparing the analysis results, or by preparing and introducing a learning model that matches the user's environment.
  • FIG. 10 is a schematic diagram for explaining the folder structure 1001 of the fourth embodiment.
  • Embodiments 1 to 3 The relationship between folders and images described in Embodiments 1 to 3 is based on the premise that images are registered directly under folders, such as two images 1012 registered in a folder 1011 .
  • a function is added that allows hierarchical registration of at least one folder.
  • a subfolder 1022 and a subfolder 1023 are registered under the folder 1021, two images 1024 are registered under the subfolder 1022, and an image 1025 is registered under the subfolder 1023.
  • two images 1032 and a subfolder 1033 are registered under the folder 1031 , and two images 1034 are registered under the subfolder 1033 .
  • folders and subfolders are collectively called folders, and folders and images are collectively called elements.
  • FIG. 10 shows an example of creating multiple top-level folders
  • the top-level folder may be limited to one and all elements may be registered under the structure.
  • the folder management unit 211 further has functions for creating, setting, deleting, and displaying a list of subfolders. Further, the user is presented with a subfolder creation screen, which will be described later with reference to FIG. 11, and a folder editing screen, which will be described later with reference to FIG.
  • the user designates a higher-level folder, displays a subfolder creation screen, and inputs a subfolder name and image resolution as subfolder setting information on the subfolder creation screen.
  • the subfolder setting information may include information such as an execution memo input when the subfolder was created, creation date and time for subfolder management, and access date and time.
  • the subfolder creation screen 1101 includes a folder name display field 1131 , a subfolder name input field 1111 , an image resolution input field 111 , an OK button 1121 and a cancel button 1122 .
  • the user inputs a name such as the name of the inspection target that makes it easy to identify the inspection object in the subfolder name input field 1111, and inputs the image resolution to be applied to the images registered in the subfolder in the image resolution input field 1112. input.
  • the image resolution input field 1112 the image resolution specified by the user and set in the upper folder displayed in the folder name display field 1131 is automatically input as an initial value, and the user changes the initial value. be able to.
  • the OK button 1121 is a button for confirming and saving the subfolder name input in the subfolder name input field 1111 and the image resolution input in the image resolution input field 1112 .
  • a cancel button 1122 is a button for canceling the subfolder name input in the subfolder name input field 1111 and the image resolution input in the image resolution input field 1112 and returning to the image list screen 301 .
  • the folder edit screen 1201 includes a folder name input field 1211 , an image resolution input field 1212 , a check box 1213 reflecting the lower elements of the image resolution, an OK button 1221 and a cancel button 1222 .
  • the user can check the folder name input field 1211 and the image resolution input field 1212 in which the folder setting information entered when creating the folder is reflected, and change them as necessary. Further, the user can set whether or not to reflect the image resolution input in the image resolution input field 1212 to the subfolder by using a check box 1213 for reflecting the changed image resolution to subfolders.
  • the check box 1213 is checked, the image resolution entered in the image resolution input field 1212 is automatically applied to all subordinate folders and images of the folder being edited.
  • the image resolution input in the input field 1212 is not reflected, but is reflected in folders and images newly registered under the folder being edited.
  • An OK button 1221 is a button for confirming the setting of the check box 1213 and reflecting and saving the image resolution input in the image resolution input field 1212 in the subfolder.
  • a cancel button 1222 is a button for canceling the setting of the check box 1213 and returning to the image list screen 301 in FIG.
  • the images registered in the folder are images after synthesis, but individual images before synthesis may be registered as they are.
  • an example has been described in which the image resolution set for the folder is applied to the image when the image is registered in the folder.
  • an image resolution input screen may be displayed, the initial value of the image resolution may be entered in an editable input field, and the user may be asked to edit and consent. .
  • the present invention supplies a program that implements one or more functions of each embodiment to a system or device via a network or a storage medium, and one or more processors of the computer of the system or device reads and executes the program. It can also be realized by processing to The invention can also be implemented by a circuit (eg, an ASIC) that implements one or more functions.
  • a circuit eg, an ASIC
  • the disclosure of this specification includes the following image processing device, image processing method, and program.
  • Folder management means for creating and setting a folder for managing images for detecting deformation of an inspection object; and image management means for setting images to be managed in the folder, wherein said folder management means sets the resolution of images managed by said folder, and said image management means applies the resolution set by said folder management means to images managed by said folder. processing equipment.
  • the image processing device according to any one of 3.
  • folder setting saving means for saving settings of the folder; an image storage means for storing settings of the image;
  • the image processing apparatus according to configuration 2, further comprising analysis result storage means for storing the result of the image analysis.
  • the folder management means sets the resolution of the images managed by the folder according to a user operation,
  • the image management means applies the resolution to images managed by the folder, Configuration 2 or 5, wherein the image analysis means executes the image analysis using a learning model for executing image analysis by inputting the image and a resolution applied to the image.
  • image processing device [Configuration 7]
  • the image analysis means is characterized in that the image analysis is performed according to the actual size information of the deformation of the object to be inspected by giving the resolution of the image as one of the parameters to the learning model used for the image analysis.
  • the image processing apparatus according to any one of configurations 2, 5, and 6.
  • the folder management means displays a list of images managed in the folder, The image processing apparatus according to any one of configurations 2, 5, 6, and 7, wherein the image analysis means displays an image analysis result of the image.
  • the image processing apparatus according to any one of configurations 1 to 8, wherein the folder management means displays a screen for inputting a folder name and resolution as settings for the folder.
  • the folder management means displays a screen for selecting an image analysis method by the image analysis means as settings for the folder, Any one of configurations 2, 5, 6, 7, and 8, wherein the image analysis method includes a learning model for executing the image analysis by inputting the image, and a resolution given to the learning model. 10.
  • the image processing device according to claim 1.
  • the image processing apparatus selects the image analysis method from the name of the inspection object and displays it on the screen.
  • the folder management means displays a warning when the resolution input to the screen deviates from a standard value or recommended value, or inputs a standard value or recommended value to the screen.
  • 9. The image processing apparatus according to 9.
  • the image processing device according to any one of .
  • the image processing apparatus according to any one of configurations 1 to 13, wherein the folder management means creates and sets folders in a plurality of hierarchical structures for managing images for detecting the deformation. .
  • the folder management means is characterized in that the resolution of the image set in the upper folder is automatically input to a screen for creating a lower folder that is a lower layer than the upper folder. 15.
  • the image processing device according to configuration 14.
  • the folder management means responds to an instruction to apply the edited resolution to a lower folder that is a layer below the higher folder. 16.
  • the image processing apparatus according to configuration 14 or 15, wherein the edited resolution is automatically reflected in all subordinate folders.
  • [Configuration 17] A first step in which the folder management means creates and sets a folder for managing images for detecting deformation of an inspection object; a second step in which the image management means sets the images to be managed in the folder; In the first step, the resolution of the images managed by the folder is set, and in the second step, the resolution set by the folder management means is applied to the images managed by the folder. image processing method. [Configuration 18] 17. A program for causing a computer to function as each means of the image processing apparatus according to any one of Configurations 1 to 16.

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WO2018037689A1 (ja) * 2016-08-22 2018-03-01 富士フイルム株式会社 画像処理装置及び画像処理方法
WO2020121564A1 (ja) * 2019-07-04 2020-06-18 株式会社日立ハイテク 寸法計測装置、寸法計測プログラム及び半導体製造システム
JP2021124933A (ja) * 2020-02-05 2021-08-30 株式会社日立製作所 画像を生成するシステム
JP2021140739A (ja) * 2020-02-28 2021-09-16 株式会社Pros Cons プログラム、学習済みモデルの生成方法、情報処理方法及び情報処理装置

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018037689A1 (ja) * 2016-08-22 2018-03-01 富士フイルム株式会社 画像処理装置及び画像処理方法
WO2020121564A1 (ja) * 2019-07-04 2020-06-18 株式会社日立ハイテク 寸法計測装置、寸法計測プログラム及び半導体製造システム
JP2021124933A (ja) * 2020-02-05 2021-08-30 株式会社日立製作所 画像を生成するシステム
JP2021140739A (ja) * 2020-02-28 2021-09-16 株式会社Pros Cons プログラム、学習済みモデルの生成方法、情報処理方法及び情報処理装置

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