Multiscale geometrical feature extraction and object recognition with wavelets and morphology
In this work, a novel method of multiscale geometric feature extraction and object recognition is developed. In particular, the new representation should have the following characteristics. First, the coarse scale features should have a geometric interpretation so that the overall geometry of the object is discernible from just these features. Second, the presence of fine scale detail should not change the coarse scale representation. These two goals are not achieved by current techniques which are based on error as measured by the L/sup 2/ norm. Two methods to accomplish these goals are presented. In the first, morphological filtering and wavelet networks are used. In the second, the correlation criteria of the matching pursuit algorithm of Mallat and Zhang (1993) is modified to obtain a variable, high resolution matching pursuit.