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公开(公告)号：US10937150B2
公开(公告)日：2021-03-02
申请号：US16022074
申请日：2018-06-28
Applicant: General Electric Company
Inventor： Huan Tan , Arpit Jain , Gyeong Woo Cheon , Ghulam Ali Baloch , Jilin Tu , Weina Ge , Li Zhang
Abstract: A method and system, the method including receiving semantic descriptions of features of an asset extracted from a first set of images; receiving a model of the asset, the model constructed based on a second set of a plurality images of the asset; receiving, based on an optical flow-based motion estimation, an indication of a motion for the features in the first set of images; determining a set of candidate regions of interest for the asset; determining a region of interest in the first set of images; iteratively determining a matching of features in the set of candidate regions of interest and the determined region of interest in the first set of images to generate a record of matches in features between two images in the first set of images; and displaying a visualization of the matches in features between two images in the first set of images.
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公开(公告)号：US10600194B2
公开(公告)日：2020-03-24
申请号：US15685867
申请日：2017-08-24
Applicant: General Electric Company
Inventor： Ming-Ching Chang , Junli Ping , Eric Michael Gros , Arpit Jain , Peter Henry Tu
IPC: G06T7/55 , G01C11/02 , G05D1/00 , G06T17/00 , G06T7/521 , G05D1/10 , G06T5/00 , G06T7/00 , G08G5/00 , G06T15/20 , H04L29/08
Abstract: The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.
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公开(公告)号：US20190145902A1
公开(公告)日：2019-05-16
申请号：US15809531
申请日：2017-11-10
Applicant: General Electric Company
Inventor： Huan Tan , Li Zhang , Romano Patrick , Arpit Jain , Guangliang Zhao , Jilin Tu
Abstract: An asset inspection system includes a robot and a server. The robot collects inspection data corresponding to an asset. The server, includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the inspection data from the robot, display the inspection data via the user-interface, receive feedback on the inspection data via the user interface, generate a human-assisted inspection based on the received feedback, analyze the inspection data via a trained model, generate an automated inspection based on the analysis by the trained model, combine the automated inspection and the human-assisted inspection to generate an inspection report, and transmit the inspection report for review.
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公开(公告)号：US20180342069A1
公开(公告)日：2018-11-29
申请号：US15605243
申请日：2017-05-25
Applicant: General Electric Company
Inventor： Ser Nam Lim , David Scott Diwinsky , Wei Wang , Swaminathan Sankaranarayanan , Xiao Bian , Arpit Jain
Abstract: A system includes one or more processors configured to analyze obtained image data representing a rotor blade to detect a candidate feature on the rotor blade and determine changes in the size or position of the candidate feature over time. The one or more processors are configured to identify the candidate feature on the rotor blade as a defect feature responsive to the changes in the candidate feature being the same or similar to a predicted progression of the defect feature over time. The predicted progression of the defect feature is determined according to an action-guidance function generated by an artificial neural network via a machine learning algorithm. Responsive to identifying the candidate feature on the rotor blade as the defect feature, the one or more processors are configured to automatically schedule maintenance for the rotor blade, alert an operator, or stop movement of the rotor blade.
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公开(公告)号：US20180253866A1
公开(公告)日：2018-09-06
申请号：US15495313
申请日：2017-04-24
Applicant: General Electric Company
Inventor： Arpit Jain , Swaminathan Sankaranarayanan , David Scott Diwinsky , Ser Nam Lim , Kari Thompson
CPC classification number: G06N7/005 , G06K9/6267 , G06K9/628 , G06N3/0454 , G06N3/0472 , G06N3/0481 , G06N3/084 , G06N3/088
Abstract: A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image. The method also includes selecting, for each of two or more of the pixels, an object class represented by the pixel by comparing the object class probabilities of the pixels with each other, determining an error associated with the object class that is selected for each pixel of the two or more pixels, determining one or more image perturbations by back-propagating the errors associated with the object classes selected for the pixels of the first input image through the layers of the neural network without modifying the neural network, and modifying a second input image by applying the one or more image perturbations to one or more of the first input image or the second input image prior to providing the second input image to the neural network for examination by the neurons in the neural network for automated object recognition in the second input image.
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Patent Agency Ranking
公开(公告)号：US11527025B2
公开(公告)日：2022-12-13
申请号：US17091748
申请日：2020-11-06
Applicant: General Electric Company
Inventor： Habib K Abi-Rached , Achalesh Kumar , Arpit Jain , Mohammed Yousefhussien , Tapan Shah
Abstract: According to some embodiments, a system and method are provided comprising a vegetation management module to receive image data from an image source; a memory for storing program instructions; a vegetation management processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive first image data and second image data for an area of interest; overlay the first image data over the second image data to generate an overlaid image; receive feeder attribute data for at least one feeder in the overlaid image; generate a risk score for the at least one feeder based in part on the received feeder attribute data; and generate a visualization based on the at least one feeder and the generated risk score. Numerous other aspects are provided.
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公开(公告)号：US10755401B2
公开(公告)日：2020-08-25
申请号：US16208668
申请日：2018-12-04
Applicant: General Electric Company
Inventor： Xiao Bian , Arpit Jain , David Scott Diwinsky , Bernard Patrick Bewlay , Steeves Bouchard , Jean-Philippe Choiniere , Marc-Andre Marois , Stephane Harel , John Karigiannis
Abstract: An inspection system includes one or more imaging devices and one or more processors. The imaging devices generate a first set of images of a work piece at a first position relative to the work piece and a second set of images of the work piece at a second position relative to the work piece. At least some of the images in the first and second sets are acquired using different light settings. The processors analyze the first set of images to generate a first prediction image associated with the first position, and analyze the second set of images to generate a second prediction image associated with the second position. The first and second prediction images include respective candidate regions. The processors merge the first and second prediction images to detect at least one predicted defect in the work piece depicted in at least one of the candidate regions.
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公开(公告)号：US20200175703A1
公开(公告)日：2020-06-04
申请号：US16783923
申请日：2020-02-06
Applicant: General Electric Company
Inventor： Ming-Ching Chang , Junli Ping , Eric Michael Gros , Arpit Jain , Peter Henry Tu
IPC: G06T7/55 , G01C11/02 , G05D1/00 , G06T17/00 , G06T7/521 , G05D1/10 , G06T5/00 , G06T7/00 , G08G5/00
Abstract: The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.
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公开(公告)号：US10546242B2
公开(公告)日：2020-01-28
申请号：US15495313
申请日：2017-04-24
Applicant: General Electric Company
Inventor： Arpit Jain , Swaminathan Sankaranarayanan , David Scott Diwinsky , Ser Nam Lim , Kari Thompson
Abstract: A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image. The method also includes selecting, for each of two or more of the pixels, an object class represented by the pixel by comparing the object class probabilities of the pixels with each other, determining an error associated with the object class that is selected for each pixel of the two or more pixels, determining one or more image perturbations by back-propagating the errors associated with the object classes selected for the pixels of the first input image through the layers of the neural network without modifying the neural network, and modifying a second input image by applying the one or more image perturbations to one or more of the first input image or the second input image prior to providing the second input image to the neural network for examination by the neurons in the neural network for automated object recognition in the second input image.
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公开(公告)号：US20190066283A1
公开(公告)日：2019-02-28
申请号：US15684695
申请日：2017-08-23
Applicant: General Electric Company
Inventor： Eric Michael Gros , Junli Ping , Arpit Jain , Ming-Ching Chang , Peter Henry Tu
Abstract: The present approach relates to the use of a point cloud of an object to initialize or seed a space carving technique used to generate a 3D model of the object. In one implementation, feature matching is performed on 2D images, with matched features constituting the points of a point cloud model. The point cloud generated in this manner, is one input of a foreground/background segmentation algorithm, which generates a set of segmented 2D images used by a space carving routine to generate a 3D model of the object.
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