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公开(公告)号：US11301977B2
公开(公告)日：2022-04-12
申请号：US16845324
申请日：2020-04-10
Applicant: General Electric Company
Inventor： Alberto Santamaria-Pang , Yousef Al-Kofahi , Aritra Chowdhury , Shourya Sarcar , Michael John MacDonald , Peter Arjan Wassenaar , Patrick Joseph Howard , Bruce Courtney Amm , Eric Seth Moderbacher
Abstract: 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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2.发明申请公开(公告)号：US20180121760A1
公开(公告)日：2018-05-03
申请号：US15796379
申请日：2017-10-27
Applicant: General Electric Company
Inventor： Alberto Santamaria-Pang , Daniel Eugene Meyer , Michael Ernest Marino , Qing Li , Dmitry V. Dylov , Aritra Chowdhury
CPC classification number: G06K9/6256 , G06K9/44 , G06K9/6267 , G06K9/6274 , G06K2209/05 , G06N3/04 , G06N3/0454 , G06N3/08 , G06N3/084 , G06T7/0012 , G06T7/11 , G06T7/155 , G06T2200/04 , G06T2207/10056 , G06T2207/10064 , G06T2207/30016 , G06T2207/30101 , G06T2207/30172 , G09B23/30 , G09B23/303 , G16H50/20 , G16H50/50
Abstract: The present approach relates to the use of trained artificial neural networks, such as convolutional neural networks, to classify vascular structures, such as using a hierarchical classification scheme. In certain approaches, the artificial neural network is trained using training data that is all or partly derived from synthetic vascular representations.
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公开(公告)号：US20210319544A1
公开(公告)日：2021-10-14
申请号：US16845324
申请日：2020-04-10
Applicant: General Electric Company
Inventor： Alberto Santamaria-Pang , Yousef Al-Kofahi , Aritra Chowdhury , Shourya Sarcar , Michael John MacDonald , Peter Arjan Wassenaar , Patrick Joseph Howard , Bruce Courtney Amm , Eric Seth Moderbacher
Abstract: 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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公开(公告)号：US10740651B2
公开(公告)日：2020-08-11
申请号：US15796379
申请日：2017-10-27
Applicant: General Electric Company
Inventor： Alberto Santamaria-Pang , Daniel Eugene Meyer , Michael Ernest Marino , Qing Li , Dmitry V. Dylov , Aritra Chowdhury
IPC: G06K9/00 , G06K9/62 , G06N3/08 , G06N3/04 , G09B23/30 , G06T7/00 , G16H50/20 , G06K9/44 , G06T7/11 , G06T7/155 , G16H50/50
Abstract: The present approach relates to the use of trained artificial neural networks, such as convolutional neural networks, to classify vascular structures, such as using a hierarchical classification scheme. In certain approaches, the artificial neural network is trained using training data that is all or partly derived from synthetic vascular representations.
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