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:
Jul. 23, 2019

Filed:

Feb. 23, 2017
Applicant:

Altumview Systems Inc., Burnaby, CA;

Inventors:

Xing Wang, Port Coquitlam, CA;

Him Wai Ng, Burnaby, CA;

Jie Liang, Coquitlam, CA;

Assignee:

AltumView Systems Inc., Port Moody, BC, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/20 (2006.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06N 3/04 (2006.01); G06N 3/06 (2006.01); G06N 3/08 (2006.01); G06T 5/50 (2006.01); G06T 7/11 (2017.01); G06T 7/246 (2017.01);
U.S. Cl.
CPC ...
G06N 3/04 (2013.01); G06K 9/00228 (2013.01); G06K 9/00268 (2013.01); G06K 9/00275 (2013.01); G06K 9/00288 (2013.01); G06K 9/2054 (2013.01); G06K 9/4628 (2013.01); G06K 9/6232 (2013.01); G06K 9/6256 (2013.01); G06K 9/6274 (2013.01); G06N 3/0454 (2013.01); G06N 3/06 (2013.01); G06N 3/08 (2013.01); G06T 5/50 (2013.01); G06T 7/11 (2017.01); G06T 7/248 (2017.01); G06K 9/00402 (2013.01); G06K 9/6289 (2013.01); G06K 2009/00322 (2013.01); G06K 2209/15 (2013.01); G06T 2200/28 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/20224 (2013.01); G06T 2207/30201 (2013.01); G06T 2210/12 (2013.01);
Abstract

Embodiments of a convolutional neural network (CNN) system based on using resolution-limited small-scale CNN modules are disclosed. In some embodiments, a CNN system includes: a receiving module for receiving an input image of a first image size, the receiving module can be used to partition the input image into a set of subimages of a second image size; a first processing stage that includes a first hardware CNN module configured with a maximum input image size, the first hardware CNN module is configured to sequentially receive the set of subimages and sequentially process the received subimages to generate a set of outputs; a merging module for merging the sets of outputs into a set of merged feature maps; and a second processing stage for receiving the set of feature maps and processing the set of feature maps to generate an output including at least one prediction on the input image.


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