The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 15, 2017

Filed:

Apr. 14, 2014
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Sean Ryan Francesco Fanello, Genoa, IT;

Cem Keskin, Cambridge, GB;

Pushmeet Kohli, Cambridge, GB;

Shahram Izadi, Cambridge, GB;

Jamie Daniel Joseph Shotton, Cambridge, GB;

Antonio Criminisi, Cambridge, GB;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/52 (2006.01); G06K 9/00 (2006.01); G06K 9/40 (2006.01); G06K 9/62 (2006.01); G06T 5/00 (2006.01); G06T 5/20 (2006.01); H04N 5/217 (2011.01);
U.S. Cl.
CPC ...
G06K 9/52 (2013.01); G06K 9/0051 (2013.01); G06K 9/40 (2013.01); G06K 9/6268 (2013.01); G06T 5/002 (2013.01); G06T 5/20 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); H04N 5/217 (2013.01);
Abstract

Filtering sensor data is described, for example, where filters conditioned on a local appearance of the signal are predicted by a machine learning system, and used to filter the sensor data. In various examples the sensor data is a stream of noisy video image data and the filtering process denoises the video stream. In various examples the sensor data is a depth image and the filtering process refines the depth image which may then be used for gesture recognition or other purposes. In various examples the sensor data is one dimensional measurement data from an electric motor and the filtering process denoises the measurements. In examples the machine learning system comprises a random decision forest where trees of the forest store filters at their leaves. In examples, the random decision forest is trained using a training objective with a data dependent regularization term.


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