Iterative Markovian estimation of mass functions in Dempster Shafer evidence theory: application to multisensor image segmentation

Layachi Bentabet; Maodong Jiang

doi:10.1117/12.641257

7 February 2006 Iterative Markovian estimation of mass functions in Dempster Shafer evidence theory: application to multisensor image segmentation

Layachi Bentabet, Maodong Jiang

Author Affiliations +

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 606402 (2006) https://doi.org/10.1117/12.641257
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Mass functions estimation is a key issue in evidence theory-based segmentation of multisensor images. In this paper, we generalize the statistical mixture modeling and the Bayesian inference approach in order to quantify the confidence level in the context of Dempster-Shafer theory. We demonstrate that our model assigns confidence levels in a relevant manner. Contextual information is integrated using a Markovian field that is adapted to handle compound hypotheses. The multiple sensors are assumed to be corrupted by different noise models. In this case, we show the interest of using a flexible Dirichlet distribution to model the data. The effectiveness of our method is demonstrated on synthetic and radar and SPOT images.

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Layachi Bentabet and Maodong Jiang "Iterative Markovian estimation of mass functions in Dempster Shafer evidence theory: application to multisensor image segmentation", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 606402 (7 February 2006); https://doi.org/10.1117/12.641257

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