A wavelet transform based multiresolution edge detection and classification schema

In this work a new multiresolution method to detect and classify edges appearing in images has been proposed. The edge detection and classification schema is based on the analysis of the date obtained by a multiresolution image analysis using Mallat and Zhong's wavelet. Multiresolution analysis allows to detect edges of different relevance at different scales, as well as to obtain other important aspects of the detected edge. The Discrete Wavelet Transform proposed by Mallat and Zhong has been used for detection and classification purposes. The classification schema developed is based oil a simple polynomial-fitting algorithm. Analyzing properties of the fitted polynomial we are able to classify several edge types. The robustness of the proposed method has been tested with different geometrical contour types appeared in the literature. A real edge type has also been introduced: the 'noise', that allow LIS to implement a novel noise-filtering algorithm simply by eliminating the points belonging to this class. The proposed classification schema Could be generalized to real edge types: shadows, corners, etc.