Microsoft Technology Licensing, LLC

Simon Stachniak

Craig C Peeper

Johnny Chung Lee

Tommer Leyvand

Shahram Izadi

Jinyu Li

Eddie L Mays

Lin Liang

Casey Leon Meekhof

Richard Henry Irving

Emad Barsoum

Tim Keosababian

Russ Glaser

Ryan Gunn

Toan Huynh

Alan Stuart

James Waletzky

Ajay Ramjee

Tommy Leyvand

Xin Tong

Stephen S Lin

Vladimir Tankovich

Yichen Wei

Cem Keskin

Shao Liu

Christoph Rhemann

Yue Dong

Sean Ryan Francesco Fanello

Vijay Baiyya

Matthew Adam Simari

Yao Shen

Tommer Leyand

Ben Butler

Jian Shi

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.

Machine vision processing system

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.

Real-time detection of object scanability

An initial candidate foreground region is identified within an infrared image that includes pixels exhibiting infrared intensity values within a pre-defined range. A depth of surfaces within the initial candidate foreground region is estimated based on infrared intensity values the pixels of the initial candidate foreground region. The initial candidate foreground region is expanded to an expanded candidate foreground region based on a body-model estimate. The body model estimate is seeded with one or more of the initial candidate foreground region, the depth of surfaces, and/or a face of a human subject identified by facial recognition. Each pixel of the infrared image is identified as either a foreground pixel or a background pixel based on a distance of that pixel relative to the expanded candidate foreground region. Pixels identified as background pixels may be modified within a corresponding visible light image.

Distinguishing foreground and background with inframed imaging

A signal encoding an infrared (IR) image including a plurality of IR pixels is received from an IR camera. Each IR pixel specifies one or more IR parameters of that IR pixel. IR-skin pixels that image a human hand are identified in the IR image. For each IR-skin pixel, a depth of a human hand portion imaged by that IR-skin pixel is estimated based on the IR parameters of that IR-skin pixel. A skeletal hand model including a plurality of hand joints is derived. Each hand joint is defined with three independent position coordinates inferred from the estimated depths of each human hand portion.

Two-dimensional infrared depth sensing

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.

Biometric recognition

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.

Methods and systems for determining and tracking extremities of a target

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 model may be adjusted based on a location or position of one or more extremities estimated or determined for a human target in the grid of voxels. The model may also be adjusted based on a default location or position of the model in a default pose such as a T-pose, a DaVinci pose, and/or a natural pose.

Systems and methods for tracking a model

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Kirkland, WA, United States of America

Simon Stachniak