Using Neural Network to Detect Dominat Points in Chain-Coded Contours

A novel approach for dominant point detection in chain-coded contours is presented. Classical operations, such as computing a measurement of the curvature from the (x,y) co-ordinates of the contour points, finding curvature maxima, etc., are substituted by a neural network that traverses the contour, and gives a measurement of the relevance of every point. Further and straight-forward processing of the network output provides the dominant points. Two translations of the Freeman chain-code are presented, that easily provide the network input from the chain link values. Results with real and test images are presented that show the feasibility of the proposed algorithm. The simulation of the neural computations on a sequential machine makes the execution time of this algorithm of the same order as that of existing algorithms, but the cost can be significantly reduced by executing these computations on a neural-oriented hardware.