STOCHASTIC MODEL-BASED IMAGE SEGMENTATION USING FUNCTIONAL APPROXIMATION

An unsupervised segmentation technique is presented that is based on a layered statistical model for both region shapes and the region internal texture signals. While the image partition is modelled as a sample of a Gibbs/Markov random field, the texture inside each image segment is described using functional approximation. The segmentation and the unknown parameters are estimated through iterative optimization of an MAP objective function. The obtained results are subjectively agreeable and well suited for the requirements of region-oriented transform image coding.