Segmentation of noisy images using nonstationary Markov fields
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The purpose of this thesis is to develop an algorithm for segmenting images corrupted by a high level of noise with different characteristics. In particular the images considered are composed of several regions describing different objects and background. The algorithm described is based on a Markov Random Field (MRF) model of the image and uses Kalman Filtering (KF) techniques and Dynamic Prograrnrning (DP) in order to smooth within the regions. The theoretical background for one dimensional and two dimensional data which have different characteristics and simulation results are presented, with examples of synthetic data and underwater images.