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Применить Всего найдено 4. Отображено 3.
03-03-2022 дата публикации

INSPECTION SYSTEMS AND METHODS INCLUDING IMAGE RETRIEVAL MODULE

Номер: US20220067082A1
Автор: Bewlay Bernard Patrick, Bian Xiao, JAIN Arpit, Liu Shaopeng, Parris Colin James, Sarcar Shourya, Xue Feng
Принадлежит:

A method of inspecting a component using an image retrieval module includes storing an inspection image file in a memory and identifying a region of interest in the inspection image file. The method further includes accessing a database storing image files and determining feature vectors associated with the image files. The method also includes determining a hash code for each image file based on the feature vectors and classifying a subset of image files as relevant based on the hash codes. The method further includes sorting the subset of image files based on the feature vectors and generating search results based on the sorted subset of image files. The image retrieval module includes a convolutional neural network configured to learn from the determination of the feature vectors and increase the accuracy of the image retrieval module in classifying the image files.

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Номер записи: 1
14-10-2021 дата публикации

Systems and methods for automatic defect recognition

Номер: US20210319544A1
Автор: Alberto Santamaria-Pang, Aritra Chowdhury, Bruce Courtney Amm, Eric Seth MODERBACHER, Michael John MacDonald, Patrick Joseph Howard, Peter Arjan WASSENAAR, Shourya Sarcar, Yousef Al-Kofahi
Принадлежит: General Electric Co

An image inspection computing device is provided. The device includes a memory device and at least one processor. The at least one processor is configured to receive at least one sample image of a first component, wherein the at least one sample image of the first component does not include defects, store, in the memory, the at least one sample image, and receive an input image of a second component. The at least one processor is also configured to generate an encoded array based on the input image of the second component, perform a stochastic data sampling process on the encoded array, generate a decoded array, and generate a reconstructed image of the second component, derived from the stochastic data sampling process and the decoded array. The at least one processor is further configured to produce a residual image, and identify defects in the second component.

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Номер записи: 2
14-10-2021 дата публикации

Systems and methods for automatic defect recognition

Номер: WO2021207491A1
Автор: Alberto Santamaria-Pang, Aritra Chowdhury, Bruce Courtney Amm, Eric Seth MODERBACHER, Michael John MacDonald, Patrick Joseph Howard, Peter Arjan WASSENAAR, Shourya Sarcar, Yousef Al-Kofahi
Принадлежит: GENERAL ELECTRIC COMPANY

An image inspection computing device (104) is provided. The device (104) includes a memory device (310,410) and at least one processor (305,405). The at least one processor (305,405) is configured to receive at least one sample image of a first component (202), wherein the at least one sample image (202) of the first component (202) does not include defects (258), store, in the memory (310,410), the at least one sample image (202), and receive an input image of a second component (206,252). The at least one processor (305,405) is also configured to generate an encoded array based on the input image of the second component (206,252), perform a stochastic data sampling process on the encoded array, generate a decoded array, and generate a reconstructed image (254) of the second component, derived from the stochastic data sampling process and the decoded array. The at least one processor (305,405) is further configured to produce a residual image (210,256), and identify defects (258) in the second component.

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Номер записи: 3