Multispectral image segmentation using a multiscale model

A new approach to Bayesian image segmentation based on a novel multiscale random field (MSRF) and a new estimation approach called sequential maximum a posteriori estimation are presented. Together, the proposed estimator and model result in a segmentation algorithm which is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. A method for estimating the parameters of the multiscale model directly from the image during the segmentation process is developed