Wavelet descriptor of planar curves: theory and applications

By using the wavelet transform, the authors develop a hierarchical planar curve descriptor that decomposes a curve into components of different scales so that the coarsest scale components carry the global approximation information while the finer scale components contain the local detailed information. They show that the wavelet descriptor has many desirable properties such as multiresolution representation, invariance, uniqueness, stability, and spatial localization. A deformable wavelet descriptor is also proposed by interpreting the wavelet coefficients as random variables. The applications of the wavelet descriptor to character recognition and model-based contour extraction from low SNR images are examined. Numerical experiments are performed to illustrate the performance of the wavelet descriptor.