Localization Scale Selection for Scale-Space Segmentation

Sokratis Makrogiannis, Nikolaos G. Bourbakis

Abstract: In this work the relation between scale-space image segmentation and selection of the localization scale is examined first, and a scale selection approach is consequently proposed in the segmentation context. Considering the segmentation part, gradient watersheds are applied to the non-linear scale-space domain followed by a grouping operation. A report on localization scale selection techniques is carried out next. Furthermore a scale selection method that originates from the evolution of the probability distribution of a region uniformity measure through the generated scales is proposed. The introduced algorithm is finally compared to a previously published approach that is also introduced into the segmentation context to indicate its efficacy.