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Proceedings Paper

Multisensor image segmentation algorithms
Author(s): Rae H. Lee
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Paper Abstract

Two algorithms, maximum a posteriori (MAP) estimation and the Dempster-shafer evidential reasoning technique, for multi-sensor or multi-spectral image data fusion for image segmentation are presented and cornpared. Regions of the images observed by each sensor are modeled as noncausal Gauss Markov random fields (GMRF) and labeled images are assumed to follow a Gibbs distribution. In the Bayesian MAP approach, we use an independent opinion pool for data fusion and a deterministic relaxation to obtain the MAP solution. In practice, the Bayesian approach is too restrictive and a likelihood represented by a point probability value is usually an overstatement of what is actually known. In the Dempster-Shafer approach, we adopt Dempster's rule of combination for data fusion, using belief intervals and ignorance to represent confidence in a particular labeling and we present a new deterministic relaxation scheme that updates the belief intervals. Results obtained from mosaic images of real textures in a three hypothetical sensors problem are presented and the two algorithms are quantitatively compared.

Paper Details

Date Published: 1 October 1990
PDF: 7 pages
Proc. SPIE 1306, Sensor Fusion III, (1 October 1990); doi: 10.1117/12.21619
Show Author Affiliations
Rae H. Lee, Univ. of Southern California a (United States)


Published in SPIE Proceedings Vol. 1306:
Sensor Fusion III
Robert C. Harney, Editor(s)

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