Microsoft Technology Licensing, LLC

Jonathan Ross Hoof

Daniel Gilbert Kennett

Anis Ahmad

Beibei Cheng

Benjamin William Walker

Michael David McGrath

Michael Anthony Hall

Kameron Wade Rausch

Abdelrehim Ahmed

Soren Hannibal Nielsen

Robert Poerschke

Tommaso Checchi

Ben Walker

Henry C Sterchi

Michael David Mcgrath

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.

Silhouette-based limb finder determination

The technology described herein splits the control focus of a user interface during a sustained user interaction. A sustained user interaction is an interaction action lasting more than a threshold period of time. The sustained interaction is initiated when the user directs the control focus of an interface onto an interface object and begins an interaction with the object. Upon determining that a sustained interaction has begun, a control focus lock is executed at the point of control focus where the sustained interaction began, for example, the point where the cursor was located when the sustained interaction began. Upon termination of the sustained interaction, the primary control focus is snapped to the location of the secondary control focus and a secondary control focus is terminated until a subsequent sustained interaction is detected.

Split control focus during a sustained user interaction

A facial skin mask may be generated based on isolating a head part in a captured image, removing a first pixel that is indicative of non-skin from the head part in the captured image, and removing a second pixel that is indicative of having a high velocity from the head part in the captured image. Heart rate may be detected based on the change of color of the pixels of the generated facial skin mask.

Facial skin mask generation for heart rate detection

Aspects of the technology described herein can insert a 2D object into a 3D environment utilizing a monoscopic view with controlled convergence. An exemplary computing device may use a depth regulator to determine a depth associated with a stereoscopic view. Moreover, a view converter in the exemplary computing device can transform the stereoscopic view into a monoscopic view with a controlled convergence distance. Further, a display configurator in the exemplary computing device can cause an external object to be displayed in the monoscopic view.

Object display utilizing monoscopic view with controlled convergence

Embodiments described herein use depth images to extract user behavior, wherein each depth image specifies that a plurality of pixels correspond to a user. In certain embodiments, one or more average extremity positions of a user, which can also be referred to as average positions of extremity blobs, are extracted from a depth image. An application is then updated based on the average positions of extremity blobs.

Extraction of user behavior from depth images

An active illumination range camera that acquires a range and a picture image of a scene and provides a reflectance for a feature in the scene responsive to a distance for the feature provided by the range image, a registered irradiance for the feature provided by the picture image and registered irradiance for a region of a calibration surface corresponding to the feature provided by an image of the calibration surface acquired by the range camera.

Range camera

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.

Visualization for blood flow in skin image data

Examples are disclosed herein that relate to processing three-dimensional user input. One example provides a method of processing user input in a three-dimensional coordinate system, comprising receiving a user input of an origin reset for the three-dimensional coordinate system, responsive to receiving the user input of the origin reset, resetting an origin of the three-dimensional coordinate system, receiving three-dimensional user input in the three-dimensional coordinate system, while the origin remains set, measuring at least one three-dimensional displacement of the three-dimensional user input relative to the origin, and causing movement of a user interface element displayed in a user interface, the movement based on the at least one measured three-dimensional displacement.

Three-dimensional user input

Embodiments described herein can be used to detect holes in a subset of pixels of a depth image that has been specified as corresponding to a user, and to fill such detected holes. Additionally, embodiments described herein can be used to produce a low resolution version of a subset of pixels that has been specified as corresponding to a user, so that when an image including a representation of the user is displayed, the image respects the shape of the user, yet is not a mirror image of the user. Further, embodiments described herein can be used to identify pixels, of a subset of pixels specified as corresponding to the user, that likely correspond to a floor supporting the user. This enables the removal of the pixels, identified as likely corresponding to the floor, from the subset of pixels specified as corresponding to the user.

Depth image processing

Changes in image exposure setting may be compensated by creating a table of aggregate differences between corresponding pixels in two images and applying the table of aggregate differences to a portion of one of the two images. The images may be in any color space for visible light, or images not of visible light, such as infrared or depth images. In various configurations, the differences may be aggregated by averaging the differences for pixels of a certain value in one of the two images.

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Redmond, WA, United States of America

Jonathan Ross Hoof