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Bayesian Image Segmentation using a Dynamic Pyramidal Structure

H. Rehrauer, K. Seidel and M. Datcu
Maximum Entropy and Bayesian Methods


We present a Bayesian segmentation algorithm which is part of a fully Bayesian approach for automatic information extraction from satellite images. It was shown that pyramidal image models based on multi-scale Markov random fields in combination with a texture model yield good classification and segmentation results. The texture model is used for an initial characterization and then an optimal segmentation is inferred using the multi-scale random field defined on a pyramid structure. Segment probabilities are calculated in a fine-to-rough analysis and segmentation is performed by a rough-to-fine decision algorithm that maximizes the a posteriori probability of the pyramid. The procedure is iterated until it converges to a stable solution.
We improve the maximization procedure by optimizing the underlying pyramidal structure of the multi-scale Markov random field. Neighborhood dependencies are switched on and off according to the image data. The hierarchical organization allows a fast computation and the segmentations obtained are smooth, even at coarse scales. Additionally it has the advantage to be exactly tractable.

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  author = {H. Rehrauer and K. Seidel and M. Datcu},
  title = {Bayesian Image Segmentation using a Dynamic Pyramidal Structure},
  booktitle = {Maximum Entropy and Bayesian Methods},
  year = {1998},
  editor = {W. Linden and V.Dose and R. Fischer and R. Preuss},
  series = {Fundamental Theories of Physics},
  keywords = {remote sensing,scale space,dynamic,non-parametric,texture}