26 February 1997 Region of interest identification in unmanned aerial vehicle imagery
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Proceedings Volume 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision; (1997) https://doi.org/10.1117/12.267823
Event: 25th Annual AIPR Workshop on Emerging Applications of Computer Vision, 1996, Washington, DC, United States
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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffrey L. Solka, David J. Marchette, George W. Rogers, Evelyn C. Durling, John E. Green, D. Talsma, "Region of interest identification in unmanned aerial vehicle imagery", Proc. SPIE 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision, (26 February 1997); doi: 10.1117/12.267823; https://doi.org/10.1117/12.267823
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