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Proceedings Paper

A comparative evaluation of image background subtraction techniques
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Paper Abstract

Many applications generate digital image sequences using a lens system, a light-transducing pixel array, and sample-and-hold A/D electronics. Non-uniformity Correction (NUC) is an image processing operation that is often required in such systems. The NUC operation subtracts the background from a temporally evolving image on a pixel-by-pixel basis. Background estimation must be robust against image features, electronic glitches, and sudden changes in background. The NUC is often applied in conjunction with a mechanical image dithering that enables separation of the image from the static additive background. In real-time applications with large pixe-count focal planes, the NUC must process large data bandwidths. The NUC algorithm must be operationally efficient to process data in real time. This paper examines a number of NUC algorithms. It defines a set of performance metrics and evaluates algorithm performance in terms of trades between these metrics and processing cost. It provides a guide for selecting an appropriate NUC algorithm based on operating conditions and available compute resources.

Paper Details

Date Published: 21 September 2007
PDF: 17 pages
Proc. SPIE 6697, Advanced Signal Processing Algorithms, Architectures, and Implementations XVII, 66970E (21 September 2007); doi: 10.1117/12.732209
Show Author Affiliations
Ankit K. Jain, MIT Lincoln Lab. (United States)
Daniel V. Rabinkin, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 6697:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVII
Franklin T. Luk, Editor(s)

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