Abstract:
A system receives digital images of a geographic location, associates each digital image with ground control points in a set of reference stereo images, and associates each digital image to each other digital image via image to image tiepoints. The system updates a geometry of each image via a bundle adjustment, and uses a prioritized stacking order to establish piecewise linear seam lines between each of the images. The system finally builds a prioritized map in a mosaic space specifying the source image pixels that are used in each region of the output mosaic, and forms the mosaic image using the prioritized map.
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.
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.
Abstract:
A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.
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.
Abstract:
Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called “geo-locating”). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.
Abstract:
A system receives digital images of a geographic location, associates each digital image with ground control points in a set of reference stereo images, and associates each digital image to each other digital image via image to image tiepoints. The system updates a geometry of each image via a bundle adjustment, and uses a prioritized stacking order to establish piecewise linear seam lines between each of the images. The system finally builds a prioritized map in a mosaic space specifying the source image pixels that are used in each region of the output mosaic, and forms the mosaic image using the prioritized map.
Abstract:
The system and methods described herein operate on a plurality of images that include multiple views of the same scene, typically from slightly different viewing angles and/or lighting conditions. One of the images is selected as a reference image. For each image ray in a non-reference image, the system and methods resample a local region from the non-reference image's space to the reference image's space. The resampling is performed multiple times, each time with a different surface orientation hypothesis. The system and methods run cross-correlation style correlators on the resampled images, evaluate correlation scores for each of the resampled images, and select the surface orientation hypothesis associated with the highest correlation score. The system and methods project a peak of the correlation surface back through a geometry model for the selected surface orientation hypothesis to determine a three-dimensional (ground) location for the image ray.
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.
Abstract:
Subject matter regards generating a 3D point cloud and registering the 3D point cloud to the surface of the Earth (sometimes called “geo-locating”). A method can include capturing, by unmanned vehicles (UVs), image data representative of respective overlapping subsections of the object, registering the overlapping subsections to each other, and geo-locating the registered overlapping subsections.