Microsoft Technology Licensing, LLC

Charles Claudius Marais

Zsolt Mathe

Ryan M Geiss

Craig C Peeper

Roderick Michael Toll

John Raymond Justice

Krassimir Emilov Karamfilov

Emad Barsoum

David James Quinn

Tommer Leyvand

Szymon Piotr Stachniak

Shao Liu

Joseph C Bertolami

Nazeeh Amin Eldirghami

Parham Mohadjer

Spencer Dean Perreault

Jon D Pulsipher

Patrick Orville Cook

Michael Taylor Janney

Kien Toan Huynh

Robert John Fitzgerald

Wayne Richard Bisson

James Chadon Foster

Christian Klein

Oscar E Murillo

Julia Schwarz

Mark James Finocchio

Alex A Kipman

Mark D Schwesinger

Johnny Chung Lee

Joel Deaguero

Michael C Johnson

Michael Paul Erich Von Hippel

David Bastien

Ronald Forbes

Kevin J Endres

Joe Bertolami

Paul John Olczak

Ronald Omega Forbes, Jr

Ioana Monica Preda

Ashley Nicole Allard

Kevin Hampton Cogger

Eduardo Escardo Raffo

Matthew H Ahn

Mauro Giusti

Aresh Mishra

Color correction technology for computing and gaming systems are discussed herein which compensate for color vision deficiency among individuals. In one example, a method includes receiving a video frame having a first non-linear transfer function and processing the video frame to have a linear transfer function. The method also includes applying a color transform to the video frame having the linear transfer function to produce at least altered color appearance parameters on selected colors that increase color perceptibility of the video frame for a colorblindness condition, and processing the video frame after the color transform to have a second non-linear transfer function and produce an output video frame. The method also includes transferring the output video frame for display on a display device.

Color correction in computing systems for color vision deficiency

Methods, systems and computer program products are described herein that enable the identification and correction of incorrect and/or inconsistent tones in the bright regions in an HDR image. A bright region is identified in an image. The bright region is classified into an assigned classification. A luminance value of the bright region is determined and compared to a predefined luminance values corresponding to the classification. The luminance value of the bright region is adjusted to match the predefined luminance values where there is a mismatch. Bright regions including mismatched or incorrect luminance values may be rendered on display to include a visual indicator that such regions include mismatched luminance values.

Automated real-time high dynamic range content review system

Embodiments of the present invention enable rich control input data to control video games that are remotely executed. Rich control input includes three-dimensional image data, color video, audio, device orientation data, and touch input. A remotely-executed video game is one executed on a server or other computing device that is networked to a client device receiving the rich control input. Rich control input includes more data than can be uploaded to a game server without degrading game performance. Embodiments of the present invention preprocess the rich control data on the client and into data that may be uploaded to the game server. The rich input stream may be processed in a general way or in a game-specific way.

Client side processing of game controller input

An NUI system to provide user input to a computer system. The NUI system includes a logic machine and an instruction-storage machine. The instruction-storage machine holds instructions that, when executed by the logic machine, cause the logic machine to detect an engagement gesture from a human subject or to compute an engagement metric reflecting the degree of the subject's engagement. The instructions also cause the logic machine to direct gesture-based user input from the subject to the computer system as soon as the engagement gesture is detected or the engagement metric exceeds a threshold.

Detecting natural user-input engagement

Embodiments of the present invention split game processing and rendering between a client and a game server. A rendered video game image is received from a game server and combined with a rendered image generated by the game client to form a single video game image that is presented to a user. Game play may be controlled using a rich sensory input, such as three-dimensional image data and audio data. The three-dimensional image data describes the shape, size and orientation of objects present in a play space. The rich sensory input is communicated to a game server, potentially with some preprocessing, and is also consumed locally on the client, at least in part. In one embodiment, latency sensitive features are the only features processed on the client and rendered on the client.

Client rendering of latency sensitive game features

A depth image of a scene may be received, observed, or captured by a device. The depth image may then be analyzed to determine whether the depth image includes a human target. For example, the depth image may include one or more targets including a human target and non-human targets. Each of the targets may be flood filled and compared to a pattern to determine whether the target may be a human target. If one or more of the targets in the depth image includes a human target, the human target may be scanned. A skeletal model of the human target may then be generated based on the scan.

Body scan

A depth image of a scene may be received, observed, or captured by a device. A human target in the depth image may then be scanned for one or more body parts such as shoulders, hips, knees, or the like. A tilt angle may then be calculated based on the body parts. For example, a first portion of pixels associated with an upper body part such as the shoulders and a second portion of pixels associated with a lower body part such as a midpoint between the hips and knees may be selected. The tilt angle may then be calculated using the first and second portions of pixels.

Systems and methods for detecting a tilt angle from a depth image

An image such as a depth image of a scene may be received, observed, or captured by a device. The image may then be processed. For example, the image may be downsampled, a shadow, noise, and/or a missing potion in the image may be determined, pixels in the image that may be outside a range defined by a capture device associated with the image may be determined, a portion of the image associated with a floor may be detected. Additionally, a target in the image may be determined and scanned. A refined image may then be rendered based on the processed image. The refined image may then be processed to, for example, track a user.

Systems and methods for processing an image for target tracking

A depth image of a scene may be observed or captured by a capture device. The depth image may include a human target and an environment. One or more pixels of the depth image may be analyzed to determine whether the pixels in the depth image are associated with the environment of the depth image. The one or more pixels associated with the environment may then be discarded to isolate the human target and the depth image with the isolated human target may be processed.

Environment and/or target segmentation

Technology for testing a target recognition, analysis, and tracking system is provided. A searchable repository of recorded and synthesized depth clips and associated ground truth tracking data is provided. Data in the repository is used by one or more processing devices each including at least one instance of a target recognition, analysis, and tracking pipeline to analyze performance of the tracking pipeline. An analysis engine provides at least a subset of the searchable set responsive to a request to test the pipeline and receives tracking data output from the pipeline on the at least subset of the searchable set. A report generator outputs an analysis of the tracking data relative to the ground truth in the at least subset to provide an output of the error relative to the ground truth.

Validation analysis of human target

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Duvall, WA, United States of America

Charles Claudius Marais