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公开(公告)号：US20230146360A1
公开(公告)日：2023-05-11
申请号：US17519946
申请日：2021-11-05
Applicant: Raytheon Company
Inventor： Philip A. Sallee , Stephen J. Raif , Nicole A. Haffke
IPC: G06T3/40
CPC classification number: G06T3/4007
Abstract: Systems and methods for VIIRS image processing. The method can include receiving image data of immediately adjacent VIIRS image scans including a first image scan and a second image scan. The first image scan and the second image scan provide a partially overlapping view of a geographic area. The method can further involve resampling columns of pixels of the first image scan and the second image scan. The resampling can include selecting, in the first image scan and the second image scan, a subset of pixel values in each column that correspond to a specified geographic distance. The method can further involve upsampling the selected pixels to an equal number of pixels in each column resulting in upsampled pixel values and interpolating the upsampled pixel values to produce modified first and second image scans.
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公开(公告)号：US11170264B2
公开(公告)日：2021-11-09
申请号：US16428198
申请日：2019-05-31
Applicant: Raytheon Company
Inventor： Philip A. Sallee , Stephen J. Raif , James Talamonti
Abstract: Subject matter regards improving image segmentation or image annotation. A method can include receiving, through a user interface (UI), for each class label of class labels to be identified by the ML model and for a proper subset of pixels of the image data, data indicating respective pixels associated with the class label, partially training the ML model based on the received data, generating, using the partially trained ML model, pseudo-labels for each pixel of the image data for which a class label has not been received, and receiving, through the UT, a further class label that corrects a pseudo-label of the generated pseudo-labels.
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公开(公告)号：US20210065386A1
公开(公告)日：2021-03-04
申请号：US16557305
申请日：2019-08-30
Applicant: Raytheon Company
Inventor： Grant B. Boroughs , Stephen J. Raif , Jody D. Verret
Abstract: An image processing technique uses requirements for a geospatial distribution of image tie points for a triangulation of images. The images are correlated, thereby generating candidate tie points across the images. Statistical consistency checks are applied to the images to identify and dispose of the candidate tie points that are local outliers, and a geometric identification technique is applied to the images to identify and dispose of the candidate tie points that are global outliers. The candidate tie points that are not local outliers or global outliers are spatially down-selected such that the spatially down-selected candidate tie points satisfy the one or more requirements for the geospatial distribution.
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公开(公告)号：US11568638B2
公开(公告)日：2023-01-31
申请号：US17020300
申请日：2020-09-14
Applicant: Raytheon Company
Inventor： Wyatt D. Sharp, III , Kathryn A. Welin , Jody D. Verret , Richard W. Ely , Stephen J. Raif
Abstract: A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.
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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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Patent Agency Ranking
公开(公告)号：US20210192789A1
公开(公告)日：2021-06-24
申请号：US17020195
申请日：2020-09-14
Applicant: Raytheon Company
Inventor： Stephen J. Raif , Allen Hainline
Abstract: Subject matter regards colorizing a three-dimensional (3D) point set. A method of colorizing a 3D point can include voxelizing 3D points including the 3D point into voxels such that a voxel of the voxels including the 3D point includes a voxel subset of the 3D points, projecting the voxel subset to respective image spaces of first and second images used to generate the 3D points, and associating a color value, determined based on a respective number of pixels of the first and second images to which the voxel subset projects, with the 3D point.
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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：US10957056B1
公开(公告)日：2021-03-23
申请号：US16557305
申请日：2019-08-30
Applicant: Raytheon Company
Inventor： Grant B. Boroughs , Stephen J. Raif , Jody D. Verret
Abstract: An image processing technique uses requirements for a geospatial distribution of image tie points for a triangulation of images. The images are correlated, thereby generating candidate tie points across the images. Statistical consistency checks are applied to the images to identify and dispose of the candidate tie points that are local outliers, and a geometric identification technique is applied to the images to identify and dispose of the candidate tie points that are global outliers. The candidate tie points that are not local outliers or global outliers are spatially down-selected such that the spatially down-selected candidate tie points satisfy the one or more requirements for the geospatial distribution.
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公开(公告)号：US10930062B2
公开(公告)日：2021-02-23
申请号：US16516155
申请日：2019-07-18
Applicant: RAYTHEON COMPANY
Inventor： Jacob Wesely Gallaway , Jeremy Jens Gerhart , Stephen J. Raif , Jody Dale Verret
Abstract: A computer vision method, executed by one or more processors, for generating a single 3D model view of a geographic scene includes: receiving image data for the scene from a plurality of sensors located at different angles with respect to the geographic scene; dividing the image data into a plurality of image spatial regions; correlating the image data in each image spatial region to obtain a score for each image data in each image spatial region; grouping the image data in each image spatial region into two or more image clusters, based on the scores for each image; performing a multi-ray intersection within each image cluster to obtain a 3D reference point for each region; for each region, combining the one or more clusters, based on the 3D reference point for the region; and registering the combined clusters for each region to obtain a single 3D model view of the scene.
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公开(公告)号：US20200380304A1
公开(公告)日：2020-12-03
申请号：US16428198
申请日：2019-05-31
Applicant: Raytheon Company
Inventor： Philip A. Sallee , Stephen J. Raif , James Talamonti
Abstract: Subject matter regards improving image segmentation or image annotation. A method can include receiving, through a user interface (UI), for each class label of class labels to be identified by the ML model and for a proper subset of pixels of the image data, data indicating respective pixels associated with the class label, partially training the ML model based on the received data, generating, using the partially trained ML model, pseudo-labels for each pixel of the image data for which a class label has not been received, and receiving, through the UT, a further class label that corrects a pseudo-label of the generated pseudo-labels.
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