Unsupervised segmentation controlled by morphological contrast ext

Abstract

A novel approach for unsupervised image segmentation is described. This approach makes use of a Gaussian pyramid as multiresolution decomposition to analyze images. Compound random fields are used to model images at each resolution. The hierarchical image model is formed by a Strauss process in the lower level and a set of white Gaussian random fields in the upper level. This basic image model is adapted to the data present at each resolution. Segmentations at coarse resolutions are used to guide segmentations at finest resolutions. Segmentation quality is controlled, at each level, by means of morphological tools. The control procedure is based on the residue between the original image and a morphological center transform. This procedure checks whether the current segmentation contains all the relevant regions in the scene. If not, the algorithm introduces seeds into the segmented image in order to detect the new regions.