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公开(公告)号：US20180293734A1
公开(公告)日：2018-10-11
申请号：US15480670
申请日：2017-04-06
Applicant: General Electric Company
Inventor： Ser Nam Lim , David Diwinsky , Yen-Liang Lin , Xiao Bian
IPC: G06T7/194
Abstract: A generative adversarial network (GAN) system includes a generator neural sub-network configured to receive one or more images depicting one or more objects. The generator neural sub-network also is configured to generate a foreground image and a background image based on the one or more images that are received, the generator neural sub-network configured to combine the foreground image with the background image to form a consolidated image. The GAN system also includes a discriminator neural sub-network configured to examine the consolidated image and determine whether the consolidated image depicts at least one of the objects. The generator neural sub-network is configured to one or more of provide the consolidated image or generate an additional image as a training image used to train another neural network to automatically identify the one or more objects in one or more other images.
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公开(公告)号：US11544900B2
公开(公告)日：2023-01-03
申请号：US16935547
申请日：2020-07-22
Applicant: General Electric Company
Inventor： Yen-Liang Lin , Xia Li , James Vradenburg Miller , Walter V Dixon, III
Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a 3D building modeling module; a memory for storing program instructions; a 3D building modeling processor, coupled to the memory, and in communication with the 3D building modeling module and operative to execute program instructions to: receive a region of interest; receive an image of the region of image from a data source; generate a surface model based on the received image including one or more buildings; generate a digital height model; decompose each building into a set of shapes; apply a correction process to the set of shapes; execute a primitive classification process to each shape; execute a fitting process to each classified shape; select a best fitting model; and generate a 3D model of each building. Numerous other aspects are provided.
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Patent Agency Ranking
公开(公告)号：US11481998B2
公开(公告)日：2022-10-25
申请号：US16590917
申请日：2019-10-02
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Yen-Liang Lin , Walter V. Dixon, III , James Vradenburg Miller
Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an image data source storing image data from a plurality of images; a height map source storing height maps for an area of interest (AOI); a building footprint module; a memory; and a building footprint processor, operative to execute the program instructions to: receive image data for an AOI; receive a height map for the AOI; execute a building segmentation module to generate a building mask that indicates a presence of one or more buildings in the AOI; apply at least one clean mask process to the generated building mask to generate a clean mask; receive the clean mask at an instance building segmentation module; and execute the instance building segmentation module to generate at least one building footprint based on the clean mask and the received image data. Numerous other aspects are provided.
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公开(公告)号：US20210103726A1
公开(公告)日：2021-04-08
申请号：US16590917
申请日：2019-10-02
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Yen-Liang Lin , Walter V. Dixon , James Vradenburg Miller
Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an image data source storing image data from a plurality of images; a height map source storing height maps for an area of interest; a building footprint module; a memory storing program instructions; and a building footprint processor, coupled to the memory, and in communication with the building footprint module and operative to execute the program instructions to: receive image data for an area of interest (AOI); receive a height map for the AOI; execute a building segmentation module to generate a building mask that indicates a presence of one or more buildings in the AOI; apply at least one clean mask process to the generated building mask to generate a clean mask; receive the clean mask at an instance building segmentation module; and execute the instance building segmentation module to generate at least one building footprint based on the clean mask and the received image data. Numerous other aspects are provided.
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公开(公告)号：US10475174B2
公开(公告)日：2019-11-12
申请号：US15480670
申请日：2017-04-06
Applicant: General Electric Company
Inventor： Ser Nam Lim , David Diwinsky , Yen-Liang Lin , Xiao Bian
Abstract: A generative adversarial network (GAN) system includes a generator neural sub-network configured to receive one or more images depicting one or more objects. The generator neural sub-network also is configured to generate a foreground image and a background image based on the one or more images that are received, the generator neural sub-network configured to combine the foreground image with the background image to form a consolidated image. The GAN system also includes a discriminator neural sub-network configured to examine the consolidated image and determine whether the consolidated image depicts at least one of the objects. The generator neural sub-network is configured to one or more of provide the consolidated image or generate an additional image as a training image used to train another neural network to automatically identify the one or more objects in one or more other images.
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公开(公告)号：US10268913B2
公开(公告)日：2019-04-23
申请号：US15477517
申请日：2017-04-03
Applicant: General Electric Company
Inventor： Ser Nam Lim , Arpit Jain , David Diwinsky , Sravanthi Bondugula , Yen-Liang Lin , Xiao Bian
Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.
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公开(公告)号：US20180286034A1
公开(公告)日：2018-10-04
申请号：US15477517
申请日：2017-04-03
Applicant: General Electric Company
Inventor： Ser Nam Lim , Arpit Jain , David Diwinsky , Sravanthi Bondugula , Yen-Liang Lin , Xiao Bian
IPC: G06T7/00
CPC classification number: G06K9/00973 , G06K9/00771 , G06K9/6284 , G06K9/6296 , G06N3/0454 , G06T2207/20081 , G06T2207/20084 , G06T2207/30164
Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.
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