Abstract: A candidate human head is found in depth video using a head detector. A head region of light intensity video is spatially resolved with a three-dimensional location of the candidate human head in the depth video. Facial recognition is performed on the head region of the light intensity video using a face recognizer.

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.

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 then be determined.

Abstract: A candidate human head is found in depth video using a head detector. A head region of light intensity video is spatially resolved with a three-dimensional location of the candidate human head in the depth video. Facial recognition is performed on the head region of the light intensity video using a face recognizer.

Abstract: Allocation of memory registers for shaders by a processor is described herein. For each shader, registers are allocated based on the shader's level of complexity. Simpler shader instances are restricted to a smaller number of memory registers. More complex shader instances are allotted more registers. To do so, developers' high level shading level (HLSL) language includes template classes of shaders that can later be replaced by complex or simple versions of the shader. The HLSL is converted to bytecode that can be used to rasterize pixels on a computing device.

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 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 then be determined.

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.

Abstract: A candidate human head is found in depth video using a head detector. A head region of light intensity video is spatially resolved with a three-dimensional location of the candidate human head in the depth video. Facial recognition is performed on the head region of the light intensity video using a face recognizer.

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.

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 then be determined.

Abstract: A computer system comprises a hardware interface, a computer-memory device, an update engine, and a posture-recognition engine. The hardware interface is configured to receive depth video of an environment from a camera. The computer-memory device stores a background model of the environment preservable over a reboot of the computer system, the background model including a plurality of trusted coordinates derived from the depth video. The update engine is configured to update the background model, including moving a trusted coordinate to greater depth if an observed pixel is behind the trusted coordinate over a first duration, but retaining the trusted coordinate if the observed pixel depth is in front of the trusted coordinate over the first duration. The posture-recognition engine is configured to recognize posture of a user in front of a background portion of the video, which is bounded by the trusted coordinates of the background model.

Abstract: A method for controlling a computer system includes acquiring video of a subject, and obtaining from the video a time-resolved sequence of depth maps. An area targeting motion is selected from each depth map in the sequence. Then, a section of the depth map bounded by the area and lying in front of a plane is selected. This section of the depth map is used for fitting a geometric model of the subject.

Abstract: A scene is illuminated with modulated illumination light that reflects from surfaces in the scene as modulated reflection light. Each of a plurality of pixels of a depth camera receive the modulated reflection light and observe a phase difference between the modulated illumination light and the modulated reflection light. For each of the plurality of pixels, an edginess of that pixel is recognized, and the phase difference of that pixel is smoothed as a function of the edginess of that pixel.

Abstract: Allocation of memory registers for shaders by a processor is described herein. For each shader, registers are allocated based on the shader's level of complexity. Simpler shader instances are restricted to a smaller number of memory registers. More complex shader instances are allotted more registers. To do so, developers' high level shading level (HLSL) language includes template classes of shaders that can later be replaced by complex or simple versions of the shader. The HLSL is converted to bytecode that can be used to rasterize pixels on a computing device.

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: A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.

Abstract: A computer system comprises a hardware interface, a computer-memory device, an update engine, and a posture-recognition engine. The hardware interface is configured to receive depth video of an environment from a camera. The computer-memory device stores a background model of the environment preservable over a reboot of the computer system, the background model including a plurality of trusted coordinates derived from the depth video. The update engine is configured to update the background model, including moving a trusted coordinate to greater depth if an observed pixel is behind the trusted coordinate over a first duration, but retaining the trusted coordinate if the observed pixel depth is in front of the trusted coordinate over the first duration. The posture-recognition engine is configured to recognize posture of a user in front of a background portion of the video, which is bounded by the trusted coordinates of the background model.

Abstract: Allocation of memory registers for shaders by a processor is described herein. For each shader, registers are allocated based on the shader's level of complexity. Simpler shader instances are restricted to a smaller number of memory registers. More complex shader instances are allotted more registers. To do so, developers' high level shading level (HLSL) language includes template classes of shaders that can later be replaced by complex or simple versions of the shader. The HLSL is converted to bytecode that can be used to rasterize pixels on a computing device.

Abstract: A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.