An automated multiscale segmentation approach for color images is presented. The scale-space stack is generated us- ing the Perona-Malik diffusion approach and the watershed algo- rithm is employed to produce the regions at each scale. A minima- linking process by downward projection is carried out over the successive scales, and a region dissimilarity measure—combining scale, contrast, and homogeneity—is subsequently estimated on the finer scale (localization scale). The dissimilarity measure is es- timated as a function of two different features, i.e., the dynamics of contours and the relative entropy of color region distributions, com- bined by means of a fuzzy-rule-based system. A region-merging process is also applied to the localization scale to produce the final regions. To validate the performance of the proposed multiscale segmentation, qualitative and quantitative results are provided in comparison to its single-scale counterpart. We also deal with the topic of localization scale selection. This stage is critical for the final segmentation results and can be used as a preprocessing step for higher level computer vision applications as well. A preliminary study of localization scale selection techniques is carried out. A scale se- lection method that originates from the evolution of the probability distribution of a region homogeneity measure across the generated scales is proposed next. The proposed algorithm is finally compared to a previously reported approach to indicate its efficiency. © 2005
['Sokratis Makrogiannis', 'Iris Vanhamel', 'Spiros Fotopoulos', 'Hichem Sahli', 'Jan Cornelis']
Watershed-based multiscale segmentation method for color images using automated scale selection