Wavelet deformable model for shape description and multiscale elastic matching

In this research, we propose a hierarchical wavelet curve descriptor which decomposes a planar curve into components of different scales so that the coarsest scale components carry the global approximation information while other finer scale components contain the local detailed information. Furthermore, we interpret the wavelet coefficients as random variables, and use the deformable stochastic wavelet descriptor to model a group of shapes which have the same topological structure but may differ slightly due to local deformation. We show that this descriptor can be conveniently used in multiscale elastic matching. Local deformation can be more effectively represented by the wavelet descriptor than the conventional Fourier descriptor, since wavelet bases are well localized in both the spatial and frequency domains. Experimental results are given to illustrate the performance of the proposed wavelet descriptor, where we use a model-based approach to extract the contour of an object from noisy images.