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:
Sep. 21, 2021

Filed:

Oct. 04, 2019
Applicant:

Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US);

Inventors:

Tim Marks, Newton, MA (US);

Abhinav Kumar, Salt Lake City, UT (US);

Wenxuan Mou, Cambridge, MA (US);

Chen Feng, Cambridge, MA (US);

Xiaoming Liu, Cambridge, MA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/77 (2017.01); G06K 9/62 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 7/77 (2017.01); G06K 9/6257 (2013.01); G06N 3/04 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30196 (2013.01); G06T 2207/30201 (2013.01);
Abstract

A controller for executing a task based on probabilistic image-based landmark localization, uses a neural network, which is trained to process images of objects of a type having a structured set of landmarks to produce a parametric probability distribution defined by values of parameters for a location of each landmark in each processed image. The controller submits the set of input images to the neural network to produce the values of the parameters that define the parametric probability distribution over the location of each landmark in the structured set of landmarks of each input image. Further, the controller determines, for each input image, a global landmark uncertainty for the image based on the parametric probability distributions of landmarks in the input image and executes the task based on the parametric probability distributions of landmarks in each input image and the global landmark uncertainty of each input image.


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