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Proceedings Paper

Detection of obscured and partially covered objects using partial network matching and an image feature network-based object recognition algorithm
Author(s): Jeremy Straub
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Paper Abstract

An approach to image classification based on the analysis of the network of points generated by an image feature detection algorithm has been proposed. This network-based approach looks at the networks produced by two images and scale and then compare them, making a classification decision. This paper considers techniques to handle the problem posed by input images that are obscured or in which the target is partially covered. These approaches are compared with the base algorithm to assess the impact on performance in the general case, obscured scenarios and obstructed scenarios.

Paper Details

Date Published: 29 May 2014
PDF: 6 pages
Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 90721B (29 May 2014); doi: 10.1117/12.2050171
Show Author Affiliations
Jeremy Straub, The Univ. of North Dakota (United States)


Published in SPIE Proceedings Vol. 9072:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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