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Title: System and method for automated object detection in an image

Abstract

A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

Inventors:
; ; ; ;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1222628
Patent Number(s):
9,152,888
Application Number:
14/026,730
Assignee:
Los Alamos National Security, LLC (Los Alamos, NM)
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Sep 13
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Kenyon, Garrett T., Brumby, Steven P., George, John S., Paiton, Dylan M., and Schultz, Peter F. System and method for automated object detection in an image. United States: N. p., 2015. Web.
Kenyon, Garrett T., Brumby, Steven P., George, John S., Paiton, Dylan M., & Schultz, Peter F. System and method for automated object detection in an image. United States.
Kenyon, Garrett T., Brumby, Steven P., George, John S., Paiton, Dylan M., and Schultz, Peter F. Tue . "System and method for automated object detection in an image". United States. https://www.osti.gov/servlets/purl/1222628.
@article{osti_1222628,
title = {System and method for automated object detection in an image},
author = {Kenyon, Garrett T. and Brumby, Steven P. and George, John S. and Paiton, Dylan M. and Schultz, Peter F.},
abstractNote = {A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {10}
}

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