A neural network-based algorithm to detect dominant points from the chain-code of a contour

A new algorithm for dominant point detection in chain-coded contours is presented. The algorithm directly operates on the chain-code link values. No computation of the (x,y) co-ordinates of the contour points is done, nor any classical computation of the curvature or its derivative. Instead, a dynamic neural network traverses the contour giving a measurement of the relevance of each point, further and simple processing provides the dominant points. The network is trained with the result that a classical dominant point detection algorithm gives for the training contours, and using as training set a number of contours extracted from natural images. Results with real and test images are presented that show the reliability of the proposed algorithm. Since this algorithm is based on applying a neural network to the contour, it significantly reduces the execution time of existing dominant point detection algorithms. Computational time measurements are presented.