Abstract: A silhouette-based limb finder may be used to detect limbs from a camera image. This limb determination may be used to control an application, such as a game, or a combination with other image processing. A first distance field indicating a distance from the edge of a silhouette in an image and a second distance field indicating distance from a location in the silhouette may be used to generate a path from an extremity point on the silhouette to the location. This path then may be used to determine a limb in the silhouette. This allows tracking of limbs even for hard to detect player poses.

Abstract: A silhouette-based limb finder may be used to detect limbs from a camera image. This limb determination may be used to control an application, such as a game, or a combination with other image processing. A first distance field indicating a distance from the edge of a silhouette in an image and a second distance field indicating distance from a location in the silhouette may be used to generate a path from an extremity point on the silhouette to the location. This path then may be used to determine a limb in the silhouette. This allows tracking of limbs even for hard to detect player poses.

Abstract: A distance field approach is used to determine when we lose the line of sight from a view point to a given pixel (or voxel) in the presence of occluding pixels (voxels). Distance values computed by the propagating the distance field can be compared to linear distances. When the linear distance differs from the propagated distance value by a given amount, the pixel (voxel) can be deemed to be occluded.

Abstract: Blood flow beneath a user's skin, for example, in a user's face may be visually rendered. In some aspects, a plurality of differences is determined in the intensity of pixels of a first image and the corresponding pixels of a subsequent second image. In some aspects, this plurality of differences is enhanced to accentuate a characteristic associated with the first image and the second image. The enhanced plurality of differences is visually rendered for each subsequent comparison of pixel intensity values.

Abstract: Blood flow beneath a user's skin, for example, in a user's face may be visually rendered. In some aspects, a plurality of differences is determined in the intensity of pixels of a first image and the corresponding pixels of a subsequent second image. In some aspects, this plurality of differences is enhanced to accentuate a characteristic associated with the first image and the second image. The enhanced plurality of differences is visually rendered for each subsequent comparison of pixel intensity values.

Abstract: Blood flow beneath a user's skin, for example, in a user's face may be visually rendered. In some aspects, a plurality of differences is determined in the intensity of pixels of a first image and the corresponding pixels of a subsequent second image. In some aspects, this plurality of differences is enhanced to accentuate a characteristic associated with the first image and the second image. The enhanced plurality of differences is visually rendered for each subsequent comparison of pixel intensity values.

Abstract: A silhouette-based limb finder may be used to detect limbs from a camera image. This limb determination may be used to control an application, such as a game, or a combination with other image processing. A first distance field indicating a distance from the edge of a silhouette in an image and a second distance field indicating distance from a location in the silhouette may be used to generate a path from an extremity point on the silhouette to the location. This path then may be used to determine a limb in the silhouette. This allows tracking of limbs even for hard to detect player poses.

Abstract: Blood flow beneath a user's skin, for example, in a user's face may be visually rendered. In some aspects, a plurality of differences is determined in the intensity of pixels of a first image and the corresponding pixels of a subsequent second image. In some aspects, this plurality of differences is enhanced to accentuate a characteristic associated with the first image and the second image. The enhanced plurality of differences is visually rendered for each subsequent comparison of pixel intensity values.

Abstract: Techniques described herein use signal analysis to detect and analyze repetitive user motion that is captured in a 3D image. The repetitive motion could be the user exercising. One embodiment includes analyzing image data that tracks a user performing a repetitive motion to determine data points for a parameter that is associated with the repetitive motion. The different data points are for different points in time. A parameter signal of the parameter versus time that tracks the repetitive motion is formed. The parameter signal is divided into brackets that delineate one repetition of the repetitive motion from other repetitions of the repetitive motion. A repetition in the parameter signal is analyzed using a signal processing technique. Curve fitting and/or autocorrelation may be used to analyze the repetition.

Abstract: Techniques described herein determine a center of mass state vector based on a body model. The body model may be formed by analyzing a depth image of a user who is performing some motion. The center of mass state vector may include, for example, center-of-mass position, center-of-mass velocity, center-of-mass acceleration, orientation, angular velocity, angular acceleration, inertia tensor, and angular momentum. A center of mass state vector may be determined for an individual body part or for the body as a whole. The center of mass state vector(s) may be used to analyze the user's motion.

Abstract: Disclosed herein are systems and methods for a runtime engine for analyzing user motion in a 3D image. The runtime engine is able to use different techniques to analyze the user's motion, depending on what the motion is. The runtime engine might choose a technique that depends on skeletal tracking data and/or one that instead uses image segmentation data to determine whether the user is performing the correct motion. The runtime engine might determine how to perform positional analysis or time/motion analysis of the user's performance based on what motion is being performed.

Abstract: Techniques described herein use signal analysis to detect and analyze repetitive user motion that is captured in a 3D image. The repetitive motion could be the user exercising. One embodiment includes analyzing image data that tracks a user performing a repetitive motion to determine data points for a parameter that is associated with the repetitive motion. The different data points are for different points in time. A parameter signal of the parameter versus time that tracks the repetitive motion is formed. The parameter signal is divided into brackets that delineate one repetition of the repetitive motion from other repetitions of the repetitive motion. A repetition in the parameter signal is analyzed using a signal processing technique. Curve fitting and/or autocorrelation may be used to analyze the repetition.

Abstract: Techniques described herein determine a center of mass state vector based on a body model. The body model may be formed by analyzing a depth image of a user who is performing some motion. The center of mass state vector may include, for example, center-of-mass position, center-of-mass velocity, center-of-mass acceleration, orientation, angular velocity, angular acceleration, inertia tensor, and angular momentum. A center of mass state vector may be determined for an individual body part or for the body as a whole. The center of mass state vector(s) may be used to analyze the user's motion.