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:
- Issue Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1222628
- Patent Number(s):
- 9152888
- 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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