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:
Feb. 05, 2019

Filed:

Aug. 29, 2017
Applicant:

Toshiba Tec Kabushiki Kaisha, Tokyo, JP;

Inventors:

Hidehiko Miyakoshi, Mishima Shizuoka, JP;

Hitoshi Iizaka, Fuji Shizuoka, JP;

Hidehiro Naitou, Mishima Shizuoka, JP;

Yuichiro Hatanaka, Mishima Shizuoka, JP;

Yuta Sasaki, Izunokuni Shizuoka, JP;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/58 (2006.01); G07G 1/00 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00201 (2013.01); G06K 9/58 (2013.01); G06K 9/6215 (2013.01); G06K 9/6254 (2013.01); G06K 9/6263 (2013.01); G07G 1/0009 (2013.01); G07G 1/0036 (2013.01); G06K 2209/17 (2013.01);
Abstract

A commodity registration apparatus configured to perform object recognition includes an interface connected to receive captured images, a storage unit storing a dictionary for the object recognition, and a processor. The processor is configured to designate a learning target article for learning processing, extract, from each captured image, feature value indicating feature of an article contained in the captured image, compare each of the extracted feature values with stored feature values of the learning target article registered in the dictionary and calculate a similarity degree therebetween, generate relationship information indicating a relationship between the captured images based on the calculated similarity degrees, exclude captured images that meet a predetermined condition based on the relationship information, and execute the learning processing by adding, to the dictionary with respect to the learning target article, the feature values indicating features of the article contained in the non-excluded captured images.


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