A graph-based approach for shape skeleton analysis

Abstract

This paper presents a novel methodology to shape characterization, where a shape skeleton is modeled as a dynamic graph, and degree measurements are computed to compose a set of shape descriptors. The proposed approach is evaluated in a classification experiment which considers a generic set of shapes. A comparison with traditional shape analysis methods, such as Fourier descriptors, Curvature, Zernike moments and Multi-scale Fractal Dimension, is also performed. Results show that the method is efficient for shape characterization tasks, in spite of the reduced amount of information present in the shape skeleton. © 2009 Springer Berlin Heidelberg.