Automatic image segmentation system through iterative edge - region co-operation

In this paper, we propose an image segmentation system adapted to the uniform and/or weakly textured region extraction. The architecture of the proposed system combines two concepts. (i) The integration of the information resulting from two complementary segmentation methods: edge detection and region extraction. Thus, this allows us to exploit the advantages of each. (ii) The active perception via the intermediate of a feedback. This permits the correction and adjustment of the control parameters of the methods used. The originality of the suggested co-operation carries on the introduction of a mechanism, which checks the coherence of the results through a comparison of the two segmentations. From over-segmentation results, both methods are iterated by loosening certain constraints, until they converge towards stable and coherent results. This coherence is achieved by minimising a dissimilarity measure between the edges and the boundaries of the regions. The aim is therefore to provide the optimal solution in the sense of compatibility between the segmentation results. The system therefore uses a hybrid co-operation approach and is almost automatic and unsupervised. The performance of this approach has been measured on two remote sensing applications: agricultural landscape segmentation and forestry vegetation classification. q 2002 Published by Elsevier Science B.V.