Abstract: Techniques and constructs can determine an albedo map and a shading map from a digital image. The albedo and shading maps can be determined based at least in part on a color-difference threshold. A color shading map can be determined based at least in part on the albedo map, and lighting coefficients determined based on the color shading map. The digital image can be adjusted based at least in part on the lighting coefficients. In some examples, respective shading maps can be produced for individual color channels of the digital image. The color shading map can be produced based at least in part on the shading maps. In some examples, a plurality of regions of the digital image can be determined, as can proximity relationships between individual regions. The albedo shading maps can be determined based at least in part on the proximity relationships.

Abstract: A system and method are disclosed for determining and alerting a user as to whether an object will successfully scan before the post-processing of the scan data. In embodiments, before post-processing of the scan data begins, the scan data is processed by a machine learning algorithm which is able to determine whether and/or how likely the scan data is to return an accurate scanned reproduction of the scanned object. The machine learning algorithm may also suggest new positions for the object in the environment where the scan is more likely to be successful.

Abstract: Techniques and constructs can determine an albedo map and a shading map from a digital image. The albedo and shading maps can be determined based at least in part on a color-difference threshold. A color shading map can be determined based at least in part on the albedo map, and lighting coefficients determined based on the color shading map. The digital image can be adjusted based at least in part on the lighting coefficients. In some examples, respective shading maps can be produced for individual color channels of the digital image. The color shading map can be produced based at least in part on the shading maps. In some examples, a plurality of regions of the digital image can be determined, as can proximity relationships between individual regions. The albedo shading maps can be determined based at least in part on the proximity relationships.

Abstract: A system and method are disclosed for determining and alerting a user as to whether an object will successfully scan before the post-processing of the scan data. In embodiments, before post-processing of the scan data begins, the scan data is processed by a machine learning algorithm which is able to determine whether and/or how likely the scan data is to return an accurate scanned reproduction of the scanned object. The machine learning algorithm may also suggest new positions for the object in the environment where the scan is more likely to be successful.

Abstract: A method for biometric identification for use with a computing device is provided herein. The method includes capturing a temporal sequence of images of the face of a user at different locations within a three-dimensional interaction space. The method further includes extracting one or more face descriptors from the images and generating a biometric template compiling the face descriptors.

Abstract: A method for biometric identification for use with a computing device is provided herein. The method includes capturing a temporal sequence of images of the face of a user at different locations within a three-dimensional interaction space. The method further includes extracting one or more face descriptors from the images and generating a biometric template compiling the face descriptors.

Abstract: A method for biometric identification for use with a computing device is provided herein. The method includes capturing a temporal sequence of images of the face of a user at different locations within a three-dimensional interaction space. The method further includes extracting one or more face descriptors from the images and generating a biometric template compiling the face descriptors.

Abstract: A method for biometric identification for use with a computing device is provided herein. The method includes capturing a temporal sequence of images of the face of a user at different locations within a three-dimensional interaction space. The method further includes extracting one or more face descriptors from the images and generating a biometric template compiling the face descriptors.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may also be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities of the isolated human target may be determined and a model may be adjusted based on the location or position of the one or more extremities.

Abstract: An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A background included in the grid of voxels may then be discarded to isolate one or more voxels associated with a foreground object such as a human target and the isolated voxels associated with the foreground object may be processed.