General Electric Company

Aritra Chowdhury

Alberto Santamaria-Pang

Patrick Joseph Howard

Dmitry Vladimirovich Dylov

Yousef Ahmed Al-Kofahi

Qing Li

Michael Ernest Marino

Bruce Courtney Amm

Daniel Eugene Meyer

Michael J MacDonald

Shourya Sarcar

Eric Seth Moderbacher

Peter Arjan Wassenaar

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.

Systems and methods for automatic defect recognition

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.

Methods of systems of generating virtual multi-dimensional models using image analysis

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Albany, NY, United States of America

Aritra Chowdhury