Hierarchical segmentation using compound Gauss-Markov random fields

The authors discuss an original approach for segmenting still images. In this approach, the image is initially decomposed in several levels of different resolution. The decomposition that has been chosen is a Gaussian pyramid. At each level of the pyramid, the image is modeled by a compound Gauss-Markov random field and the segmentation is obtained by using a maximum a posteriori criterion. The segmentation is carried out first at the top level of the pyramid. Once a level (l ) has been segmented, this segmentation is projected onto the following level below it (l-1). The process is iterated until the segmentation at the bottom level (0) is performed