Microsoft Technology Licensing, LLC

Shao Liu

Craig C Peeper

Tommer Leyvand

Szymon Piotr Stachniak

Johnny Chung Lee

Johnny Hsienchow Lee

Avi Bar-Zeev

Alex Aben-Athar Kipman

Charles Claudius Marais

Joseph C Bertolami

Nazeeh Amin Eldirghami

Parham Mohadjer

Spencer Dean Perreault

Jon D Pulsipher

Patrick Orville Cook

Kien Toan Huynh

Robert John Fitzgerald

Wayne Richard Bisson

Michael Taylor Janney

James Chadon Foster

Jin Li

Philip Andrew Chou

Sudipta Sengupta

Mark James Finocchio

Jeffrey Neil Margolis

Robert L Crocco, Jr

Jason S Flaks

Dinan Srilal Gunawardena

Peter Bernard Key

Simon Stachniak

Milan Vojnovic

Michael C Johnson

Laurent Massoulie

Ronald Forbes

Ronald Omega Forbes, Jr

Mung Chiang

Tommer Leyand

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 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.

Human tracking system

Techniques are provided for synchronization of sensor signals between devices. One or more of the devices may collect sensor data. The device may create a sensor signal from the sensor data, which it may make available to other devices upon a publisher/subscriber model. The other devices may subscribe to sensor signals they choose. A device could be a provider or a consumer of the sensor signals. A device may have a layer of code between an operating system and software applications that processes the data for the applications. The processing may include such actions as synchronizing the data in a sensor signal to a local time clock, predicting future values for data in a sensor signal, and providing data samples for a sensor signal at a frequency that an application requests, among other actions.

Time synchronizing sensor continuous and state data signals between nodes across a network

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

Technology is provided for synchronization of clock information between networked devices. One or more of the devices may include one or more applications needed access to data and a common time reference between devices. In one embodiment, the devices have applications utilizing data shared in a network environment with other devices, as well as having a reference to a local clock signal on each device. A device may have a layer of code between the operating system and software applications that processes the data and maintains a remote clock reference for one or more of the other devices on the network.

Distributed robust clock synchronization

Peer-to-peer communications sessions involve the transmission of one or more data streams from a source to a set of receivers that may redistribute portions of the data stream via a set of routing trees. Achieving a comparatively high, sustainable data rate throughput of the data stream(s) may be difficult due to the large number of available routing trees, as well as pertinent variations in the nature of the communications session (e.g., upload communications caps, network link caps, the presence or absence of helpers, and the full or partial interconnectedness of the network.) The selection of routing trees may be facilitated through the representation of the node set according to a linear programming model, such as a primal model or a linear programming dual model, and iterative processes for applying such models and identifying low-cost routing trees during an iteration.

Models for routing tree selection in peer-to-peer communications

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Bellevue, WA, United States of America

Shao Liu