No abstract

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 modelbased approach to extract the contour of an object from noisy images.

By using the biorthogonal wavelet transform, we develop a hierarchical planar curve descriptor which decomposes a curve into components of different scales so that the coarsest scale carries the globaJ approximation information while other finer scale components contain the local detailed information. It is shown that the wavelet descriptor can be computed effectively and has many nice properties as a representation tool, e.g. invariance, uniqueness, and stability. We discuss several applications of the wavelet descriptor, including character recognition and shape deformation. Experimental results are given to illustrate the performance of the proposed wavelet descriptor.

No abstract