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Proceedings Paper

Distributed detection methods for displacement estimation
Author(s): Serafim N. Efstratiadis; Aggelos K. Katsaggelos
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Paper Abstract

In this paper, a distributed detection approach for displacement estimation in image sequences is presented. This method is derived from a Bayesian framework and reduces to a M-ary Hypothesis test among a representative set of possible displacement vectors. It is shown that the mean-squared error based block-matching (BM) algorithm is a special case of this general approach. In our approach, at each point of the current frame a set of overlapping localized detectors outputs a number of estimates for the displacement vector. Then, a distributed detection network is adopted for the fusion of the these estimates. Since the computational load is high, suboptimal but computationally efficient solutions are proposed. The above method gives a more accurate estimation of the displacement field and it is shown to be more robust in the presence of occlusion and noise, compared to the BM algorithm. Experimental results on video-conference image sequences are presented.

Paper Details

Date Published: 1 September 1990
PDF: 10 pages
Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); doi: 10.1117/12.35125
Show Author Affiliations
Serafim N. Efstratiadis, Northwestern Univ. (United States)
Aggelos K. Katsaggelos, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 1360:
Visual Communications and Image Processing '90: Fifth in a Series
Murat Kunt, Editor(s)

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