Proceedings PaperMultisensor image segmentation algorithms
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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.