Microsoft Technology Licensing, LLC

Apple Inc.

Lacework Inc.

Oliver M Williams

Ryan M Geiss

Johnny Hsienchow Lee

Jamie Daniel Joseph Shotton

Craig C Peeper

Tommer Leyvand

Mark James Finocchio

Robert Matthew Craig

Momin Mahmoud Al-Ghosien

Matt Bronder

Andrew William Fitzgibbon

Matthew L Bronder

Jamie Shotton

Shahram Izadi

Yijou Chen

Michael Isard

Alexandru Octavian Balan

Jason S Flaks

David Kim

David G Molyneaux

Paul Ronald Barham

Britta Silke Hummel

Abdelrehim Ahmed

Steve Hodges

Otmar Hiliges

Using real-time monitoring to inform static analysis, including: inspecting, using one or more static code analysis techniques, one or more components of a cloud deployment; detecting, using data gathered during the execution of the component in the cloud deployment, a condition; and modifying, based on the detected condition, the one or more static code analysis techniques.

Using real-time monitoring to inform static analysis

Described is a framework (or technique or method) and system for providing a distributed computing network including different types of devices. The framework allows peer devices to transmit 'packages' of information (e.g. messages) throughout the network as conceptual units of a job or data. The framework allows portable code, which is used to perform one or more jobs, to be transportable between peer devices. A device may transmit portable code describing a computation to another device, and the receiving device may then compile the code into executable form that is compatible with the architecture of the particular device. The compiled code may execute functions to perform one or more computations to fulfill job requests as part of a distributed computing protocol.

Peer-to-peer distributed computing system for heterogeneous device types

A method of tracking a subject includes receiving from a source a depth image of a scene including the subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that image the subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the subject as a model including a plurality of shapes.

Pose tracking pipeline

Systems and methods are disclosed for describing and tracking edges within the field of view of one or more imaging devices. In one example, the present system defines a row of pixels taken across a width of the edge, and then determines a binary edge descriptor for the edge by comparing at least one of grayscale values and contrast of pixels within respective pixel pairs from the row of pixels, the result of the comparisons setting bits within the binary descriptor.

Real time photometric edge description

A system and method are disclosed for recognizing and tracking a user's skeletal joints with a NUI system. The system includes one or more experts for proposing one or more skeletal hypotheses each representing a user pose within a given frame. Each expert is generally computationally inexpensive. The system further includes an arbiter for resolving the skeletal hypotheses from the experts into a best state estimate for a given frame. The arbiter may score the various skeletal hypotheses based on different methodologies. The one or more skeletal hypotheses resulting in the highest score may be returned as the state estimate for a given frame. It may happen that the experts and arbiter are unable to resolve a single state estimate with a high degree of confidence for a given frame. It is a further goal of the present system to capture any such uncertainty as a factor in how a state estimate is to be used.

System for fast, probabilistic skeletal tracking

A system and method for providing an augmented reality environment in which the environmental mapping process is decoupled from the localization processes performed by one or more mobile devices is described. In some embodiments, an augmented reality system includes a mapping system with independent sensing devices for mapping a particular real-world environment and one or more mobile devices. Each of the one or more mobile devices utilizes a separate asynchronous computing pipeline for localizing the mobile device and rendering virtual objects from a point of view of the mobile device. This distributed approach provides an efficient way for supporting mapping and localization processes for a large number of mobile devices, which are typically constrained by form factor and battery life limitations.

Distributed asynchronous localization and mapping for augmented reality

Systems and methods are disclosed for identifying objects captured by a depth camera by condensing classified image data into centroids of probability that captured objects are correctly identified entities. Output exemplars are processed to detect spatially localized clusters of non-zero probability pixels. For each cluster, a centroid is generated, generally resulting in multiple centroids for each differentiated object. Each centroid may be assigned a confidence value, indicating the likelihood that it corresponds to a true object, based on the size and shape of the cluster, as well as the probabilities of its constituent pixels.

Multiple centroid condensation of probability distribution clouds

A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.

A method of tracking a target includes receiving from a source an observed depth image of a scene including the target. Each pixel of the observed depth image is labeled as either a foreground pixel belonging to the target or a background pixel not belonging to the target. Each foreground pixel is labeled with body part information indicating a likelihood that that foreground pixel belongs to one or more body parts of the target. The target is modeled with a skeleton including a plurality of skeletal points, each skeletal point including a three dimensional position derived from body part information of one or more foreground pixels.

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San Francisco, CA, United States of America

Oliver M Williams