1
Alex Kipman, Mark Finocchio, Ryan M Geiss, Johnny Chung Lee, Charles Claudius Marais, Zsolt Mathe: Visual target tracking using model fitting and exemplar. Microsoft Corporation, Alleman Hall McCoy Russell & Tuttle, July 5, 2011: US07974443 (28 worldwide citation)

A method of tracking a target includes receiving an observed depth image of the target from a source and analyzing the observed depth image with a prior-trained collection of known poses to find an exemplar pose that represents an observed pose of the target. The method further includes rasterizing ...


2
Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M Al Ghosien, Matt Bronder, Oliver Williams, Ryan M Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio: Pose tracking pipeline. Microsoft Corporation, Alleman Hall McCoy Russell & Tuttle, October 22, 2013: US08565485 (16 worldwide citation)

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 fro ...


3
Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M Al Ghosien, Matt Bronder, Oliver Williams, Ryan M Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio: Pose tracking pipeline. Microsoft Corporation, Alleman Hall McCoy Russell & Tuttle, October 23, 2012: US08295546 (12 worldwide citation)

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 l ...


4
Alex Kipman, R Stephen Polzin, Kudo Tsunoda, Darren Bennett, Stephen Latta, Mark Finocchio, Gregory G Snook, Relja Markovic: Mapping a natural input device to a legacy system. Microsoft Corporation, Alleman Hall McCoy Russell & Tuttle, May 21, 2013: US08448094 (4 worldwide citation)

Systems and methods for mapping natural input devices to legacy system inputs are disclosed. One example system may include a computing device having an algorithmic preprocessing module configured to receive input data containing a natural user input and to identify the natural user input in the inp ...


5
Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M Al Ghosien, Matt Bronder, Oliver Williams, Ryan M Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio: Pose tracking pipeline. MICROSOFT TECHNOLOGY LICENSING, Gregg Wisdom, Judy Yee, Micky Minhas, October 11, 2016: US09465980 (2 worldwide citation)

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 identifie ...


6
Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M Al Ghosien, Matt Bronder, Oliver Williams, Ryan M Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio: Pose tracking pipeline. Microsoft Corporation, Alleman Hall McCoy Russell & Tuttle, October 8, 2013: US08553939 (2 worldwide citation)

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 fro ...


7
Mark Finocchio, Alexandru Balan, Nathan Ackerman, Jeffrey Margolis: Near-plane segmentation using pulsed light source. MICROSOFT TECHNOLOGY LICENSING, Dan Choi, Judy Yee, Micky Minhas, April 5, 2016: US09304594 (2 worldwide citation)

Methods for recognizing gestures within a near-field environment are described. In some embodiments, a mobile device, such as a head-mounted display device (HMD), may capture a first image of an environment while illuminating the environment using an IR light source with a first range (e.g., due to ...


8
Robert Matthew Craig, Tommer Leyvand, Craig Peeper, Momin M Al Ghosien, Matt Bronder, Oliver Williams, Ryan M Geiss, Jamie Daniel Joseph Shotton, Johnny Lee, Mark Finocchio: Pose tracking pipeline. Microsoft Corporation, Alleman Hall McCoy Russell & Tuttle, December 17, 2013: US08610665 (1 worldwide citation)

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 fro ...


9
Mark Finocchio, Richard E Moore, Ryan M Geiss, Jamie Shotton: Parallel processing machine learning decision tree training. MICROSOFT TECHNOLOGY LICENSING, Gregg Wisdom, Judy Yee, Micky Minhas, October 27, 2015: US09171264 (1 worldwide citation)

Embodiments are disclosed herein that relate to generating a decision tree through graphical processing unit (GPU) based machine learning. For example, one embodiment provides a method including, for each level of the decision tree: performing, at each GPU of the parallel processing pipeline, a feat ...


10
Shao Liu, Mark Finocchio, Avi Bar Zeev, Jeffrey Margolis, Jason Flaks, Robert Crocco Jr, Alex Aben Athar Kipman: Time synchronizing sensor continuous and state data signals between nodes across a network. Microsoft Technology Licensing, Brandon Roper, Judy Yee, Micky Minhas, August 25, 2015: US09116220 (1 worldwide citation)

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 subscri ...