Adaptive hierarchical algorithm for accurate image segmentation

A conceptually new algorithm is presented for segmenting textured images into regions in each of which data is modelled as one of C 2-D Markov Random Field (MRF). The algorithm is designed to operate in real time when implemented on new parallel architecture. Gaussian MRF is used to model textures in visible light images of outdoor and indoor scenes. Image segmentation is realized as a maximum likelihood estimation. To simplify the segmentation algorithm, the image is partitioned into disjoint square windows in each of which there will be one or atmost two different texture regions. In any given window the segmentation algorithm is hierarchical and uses a pyramid-like structure. This paper is an extension to material introduced in [1,2] and concentrates on exploring the segmentation accuracy of the algorithm and addressing more fully the question of how the algorithm can operate in adaptive modes when the parameters of the texture field are partially or totally unknown.