2-D Shape Decomposition into Overlapping Parts

Abstract

We propose a method to generate component-based shape descriptions by the application of a perceptual grouping approach known as tensor voting. Based on previously described results on the generation of region, curve and junction saliencies and motivated by psychological findings about shape perception, we introduce extensions by a voting between junctions to create amodal completions, by a labeling of the junctions according to a catalog of junction types, and by a traversal algorithm to collect the local information into globally consistent part decompositions. In contrast to commonly used partitioning schemes, our method is able to create layered representations of overlapping parts. We consider this a major advantage together with the use of local operations and low computational costs whereas other approaches are based on highly iterative processes.