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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Makrogiannis, S., & Bourbakis, N. (2005). Localization scale selection for scale-space segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3656 LNCS, pp. 1–8). https://doi.org/10.1007/11559573_1
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