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Proceedings Paper

Real-time image fusion and target learning and detection on a laptop attached processor
Author(s): David Fay; Paul Ilardi; Nathan Sheldon; Daniel Grau; Robert Biehl; Allen Waxman
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Paper Abstract

The theory of opponent-sensor image fusion is based on neural circuit models of adaptive contrast enhancement and opponent-color interaction, as developed and previously presented by Waxman, Fay et al. This approach can directly fuse 2, 3, 4, and 5 imaging sensors, e.g., VNIR, SWIR, MWIR, and LWIR for fused night vision. The opponent-sensor images also provide input to a point-and-click fast learning approach for target fingerprinting (pattern learning and salient feature discovery) and subsequent target search. We have recently developed a real-time implementation of multi-sensor image fusion and target learning & search on a single board attached processor for a laptop computer. In this paper we will review our approach to image fusion and target learning, and demonstrate fusion and target detection using an array of VNIR, SWIR and LWIR imagers. We will also show results from night data collections in the field. This opens the way to digital fused night vision goggles, weapon sights and turrets that fuse multiple sensors and learn to find targets designated by the operator.

Paper Details

Date Published: 25 May 2005
PDF: 12 pages
Proc. SPIE 5802, Enhanced and Synthetic Vision 2005, (25 May 2005); doi: 10.1117/12.607008
Show Author Affiliations
David Fay, BAE Systems Advanced Information Technologies (United States)
Paul Ilardi, BAE Systems Advanced Information Technologies (United States)
Nathan Sheldon, BAE Systems Advanced Information Technologies (United States)
Daniel Grau, BAE Systems Advanced Information Technologies (United States)
Robert Biehl, BAE Systems Advanced Information Technologies (United States)
Allen Waxman, BAE Systems Advanced Information Technologies (United States)


Published in SPIE Proceedings Vol. 5802:
Enhanced and Synthetic Vision 2005
Jacques G. Verly, Editor(s)

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