RANGE IMAGE SEGMENTATION USING MULTIPLE MARKOV RANDOM-FIELDS

Abstract

A method of range image segementation using four Markov random field(MRF)s is described in this paper. MRFs, are used in depth smoothing, gradient smoothing, edge detection and surface type labelling stage. First, range and its gradient images are smoothed preserving jump and roof edges respectively using line process concept one after another. Then jump and roof edges are extracted, combined and refined using penalizing undesirable edge patterns. Finally, curvatures are computed and the surface types are labeled according to the signs of principle curvatures. The surface type labels are refined using winner-takes-all layers in the last stage. The final output is a set of regions with its exact surface type. The energy function is used in order to represent constraints of each stage and the minimum energy state is found using iterative method. Several experimental results show the generality of our approach and the execution speed of the proposed method is faster than that of a typical region merging method. This promises practical applications of our method.