SHAPE CHARACTERIZATION WITH THE WAVELET TRANSFORM

We present a new approach to the problem of two-dimensional multiscale shape representation and analysis, based on the one-dimensional conti nuous wavelet transform (CWT). The shape is represented by the complex signal that describes its boundary, and the CWT is applied to this si gnal, leading to the so-called W-representation. Wavelet theory provid es the W-representation with several properties that are generally req uired from shape representation frameworks. In addition, we introduce algorithms for extracting meaningful information about the shape from its W-representation, for instance, detection of dominant points and s hape partitioning, natural scales analysis, and fractal-based analysis . The algorithms that accomplish these tasks are tested on shapes obta ined from synthetic and real images. Thus the W-representation yields a unified approach to a number of important problems of shape characte rization for purposes of machine vision. (C) 1997 Elsevier Science B.V .