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Patent Agency Ranking
公开(公告)号：US11468266B2
公开(公告)日：2022-10-11
申请号：US16586465
申请日：2019-09-27
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Stephen J. Raif , Philip A. Sallee , Jeffrey S. Klein , Steven A. Israel , Franklin Tanner , Shane A. Zabel , James Talamonti , Lisa A. Mccoy
Abstract: A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.
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公开(公告)号：US20210097344A1
公开(公告)日：2021-04-01
申请号：US16586465
申请日：2019-09-27
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Stephen J. Raif , Philip A. Sallee , Jeffrey S. Klein , Steven A. Israel , Franklin Tanner , Shane A. Zabel , James Talamonti , Lisa A. Mccoy
Abstract: A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.
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公开(公告)号：US11587323B2
公开(公告)日：2023-02-21
申请号：US16874138
申请日：2020-05-14
Applicant: Raytheon Company
Inventor： Steven A. Israel
Abstract: A machine accesses a set of image target models, each image target model being associated with model parameters, the model parameters comprising at least an operational domain, an expected input image quality, and an expected orientation. The machine receives an image for processing by one or more image target models from the set, the image including metadata specifying image parameters of the received image. The machine identifies, based on the image parameters in the metadata of the received image and the model parameters of one or more models in the set, a first subset of the set of image target models including image target models that are capable of processing the received image. The machine provides the received image to at least one image target model in the first subset.
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公开(公告)号：US11080837B2
公开(公告)日：2021-08-03
申请号：US16506218
申请日：2019-07-09
Applicant: Raytheon Company
Inventor： Steven A. Israel
Abstract: Discussed herein are architectures and techniques for improving execution or training of machine learning techniques. A method can include receiving a request for image data, the request indicating an analysis task to be performed using the requested image data, determining a minimum image quality score for performing the analysis task, issuing a request for image data associated with an image quality at last equal to, or greater than, the determined minimum image quality score, receiving, in response to the request, image data with an image quality score greater than, or equal to, the determined minimum image quality score, and providing the received image data to (a) a machine learning (ML) model executor to perform the image analysis task or (b) an ML model trainer that trains the ML model to perform the image analysis task.
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公开(公告)号：US20210012477A1
公开(公告)日：2021-01-14
申请号：US16506218
申请日：2019-07-09
Applicant: Raytheon Company
Inventor： Steven A. Israel
Abstract: Discussed herein are architectures and techniques for improving execution or training of machine learning techniques. A method can include receiving a request for image data, the request indicating an analysis task to be performed using the requested image data, determining a minimum image quality score for performing the analysis task, issuing a request for image data associated with an image quality at last equal to, or greater than, the determined minimum image quality score, receiving, in response to the request, image data with an image quality score greater than, or equal to, the determined minimum image quality score, and providing the received image data to (a) a machine learning (ML) model executor to perform the image analysis task or (b) an ML model trainer that trains the ML model to perform the image analysis task.
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