Edge detection and classification using Mallat's wavelet

We have developed an improved edge detector and classifier for grey level images using multiresolution wavelet-based analysis, particularly the wavelet introduced by Mallat (see IEEE Trans. on Patt. Anal. and Machine Intell., vol.14, no.7, p.710, 1992), specifically designed for edge detection. The edge detection algorithm has been designed based on a top-down maxima searching criterion, giving the best edge position to that obtained in the lowest scale. We have also been able to classify four different edge profiles: step, ramp, pulse and stair. The classification has been made training a neural network with the coefficients' evolution across scales at the edge localization. The results obtained with a synthetic 256/spl times/256 grey level image with four shapes, each one having a different edge profile have been presented. A perfect segmentation of the four objects is reached.<>