Time-scale energy based analysis of contours of real-world shapes

This paper presents a novel approach for the local analysis of the contour of a planar real world shape. The 1D representation of that contour is a very complicated signal with several non isolated and oscillating singularities, which represent the micro-structure of the shape. The analysis of such a signal is usually difficult because of the presence of spurious phenomena in its time-scale representation, typical of oscillating singularities. The aim of the proposed model is to exploit the time-scale behavior of the energy of wavelet coefficients to extract a particular sequence of scales where those spurious phenomena are strongly reduced. The locality of the wavelet transform is then used to segment the contour into meaningful subregions, in agreement with their local spectral properties. Experimental results show that the proposed model overcomes some limits of existing methods for the analysis of real-world shapes micro-structure.