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Proceedings Paper

Virtual confocal microscopy
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Paper Abstract

There is a need for persistent-surveillance assets to capture high-resolution, three-dimensional data for use in assisted target recognizing systems. Passive electro-optic imaging systems are presently limited by their ability to provide only 2-D measurements. We describe a methodology and system that uses existing technology to obtain 3-D information from disparate 2-D observations. This data can then be used to locate and classify objects under obscurations and noise. We propose a novel methodology for 3-D object reconstruction through use of established confocal microscopy techniques. A moving airborne sensing platform captures a sequence of geo-referenced, electro-optic images. Confocal processing of this data can synthesize a large virtual lens with an extremely sharp (small) depth of focus, thus yielding a highly discriminating 3-D data collection capability based on 2-D imagery. This allows existing assets to be used to obtain high-quality 3-D data (due to the fine z-resolution). This paper presents a stochastic algorithm for reconstruction of a 3-D target from a sequence of affine projections. We iteratively gather 2-D images over a known path, detect target edges, and aggregate the edges in 3-D space. In the final step, an expectation is computed resulting in an estimate of the target structure.

Paper Details

Date Published: 26 January 2006
PDF: 10 pages
Proc. SPIE 6056, Three-Dimensional Image Capture and Applications VII, 605607 (26 January 2006); doi: 10.1117/12.650778
Show Author Affiliations
Philip M. Hanna, Air Force Research Lab. (United States)
Brian D. Rigling, Wright State Univ. (United States)
Edmund G. Zelnio, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 6056:
Three-Dimensional Image Capture and Applications VII
Brian D. Corner; Peng Li; Matthew Tocheri, Editor(s)

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