Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection
Abstract
An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.
- Inventors:
- Issue Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1330321
- Patent Number(s):
- 9477901
- Application Number:
- 14/805,540
- Assignee:
- Los Alamos National Security, LLC (Los Alamos, NM)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06T - IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- DOE Contract Number:
- AC52-06NA25396
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2015 Jul 22
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 99 GENERAL AND MISCELLANEOUS; 97 MATHEMATICS AND COMPUTING
Citation Formats
Paiton, Dylan M., Kenyon, Garrett T., Brumby, Steven P., Schultz, Peter F., and George, John S. Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection. United States: N. p., 2016.
Web.
Paiton, Dylan M., Kenyon, Garrett T., Brumby, Steven P., Schultz, Peter F., & George, John S. Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection. United States.
Paiton, Dylan M., Kenyon, Garrett T., Brumby, Steven P., Schultz, Peter F., and George, John S. Tue .
"Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection". United States. https://www.osti.gov/servlets/purl/1330321.
@article{osti_1330321,
title = {Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection},
author = {Paiton, Dylan M. and Kenyon, Garrett T. and Brumby, Steven P. and Schultz, Peter F. and George, John S.},
abstractNote = {An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {10}
}
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