Segmentation of multi-sensor images

Abstract

Summary form only given. Regions of the images observed by each sensor have been modeled as noncausal Gaussian Markov random fields (GMRFs), and labeled images have been assumed to follow a Gibbs distribution. The region labeling algorithms then become functions of model parameters, and the multisensor image segmentation problems become inference problems, given multisensor parameter measurements and local spatial interaction evidence. Two different multisensor image segmentation algorithms, maximum a posteriori (MAP) estimation and the Dempster-Shafer evidential reasoning technique, have been developed and evaluated. The Bayesian MAP approach uses an independent opinion pool for data fusion and a deterministic relaxation to obtain the map solution. Dempster-Shafer approach uses Dempster's rule of combination for data fusion, belief intervals and ignorance to represent confidence of labeling, and a deterministic relaxation scheme that updates the belief intervals. Simulations with mosaic images of real textures and with anatomical magnetic resonance images have been carried out.<>