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. 14, 2021

Filed:

May. 21, 2020
Applicant:

Kodak Alaris Inc., Rochester, NY (US);

Inventors:

Alexander C. Loui, Rochester, NY (US);

David Kloosterman, Rochester, NY (US);

Michal Kucer, Rochester, NY (US);

Nathan Cahill, Rochester, NY (US);

David Messinger, Rochester, NY (US);

Assignee:

KODAK ALARIS INC., Rochester, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06K 9/46 (2006.01); G06T 7/136 (2017.01); G06T 7/162 (2017.01); G06T 7/12 (2017.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); G06K 9/4671 (2013.01); G06T 7/12 (2017.01); G06T 7/136 (2017.01); G06T 7/162 (2017.01); G06T 7/194 (2017.01); G06T 2207/10004 (2013.01); G06T 2207/20072 (2013.01);
Abstract

A system and method that performs iterative foreground detection and multi-object segmentation in an image is disclosed herein. A new background prior is introduced to improve the foreground segmentation results. Three complimentary methods detect and segment foregrounds containing multiple objects. The first method performs an iterative segmentation of the image to pull out the salient objects in the image. In a second method, a higher dimensional embedding of the image graph is used to estimate the saliency score and extract multiple salient objects. A third method uses a metric to automatically pick the number of eigenvectors to consider in an alternative method to iteratively compute the image saliency map. Experimental results show that these methods succeed in accurately extracting multiple foreground objects from an image.


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