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Proceedings Paper

Region of interest identification in unmanned aerial vehicle imagery
Author(s): Jeffrey L. Solka; David J. Marchette; George W. Rogers; Evelyn C. Durling; John E. Green; D. Talsma
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Paper Abstract

This paper details recent work by our group on the use of low-level features for the identification of man-made regions in unmanned aerial vehicle (UAV) imagery. Using low- level fractal-based features, the system classifies regions in the image via probability densities estimated for each class. These densities are estimated semi-parametrically, giving the system great flexibility in the functional form of the densities. This paper details some of our group's contributions to the areas of feature extraction, probability density estimation, classification, and the integration of these techniques into a user friendly environment. In addition we present some preliminary results from an ongoing large scale study involving recently collected UAV imagery.

Paper Details

Date Published: 26 February 1997
PDF: 12 pages
Proc. SPIE 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision, (26 February 1997); doi: 10.1117/12.267823
Show Author Affiliations
Jeffrey L. Solka, Naval Surface Warfare Ctr. (United States)
David J. Marchette, Naval Surface Warfare Ctr. (United States)
George W. Rogers, Naval Surface Warfare Ctr. (United States)
Evelyn C. Durling, Naval Surface Warfare Ctr. (United States)
John E. Green, Naval Surface Warfare Ctr. (United States)
D. Talsma, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 2962:
25th AIPR Workshop: Emerging Applications of Computer Vision

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