Neural Techniques for Path Linking, with Applications to Image Processing

Edge linking is a fundamental computer-vision task, yet presents difficulties arising from the lack of information in the image. Viewed as a constrained optimisation problem, it is NP hard – being isomorphic to the classical travelling salesman problem. Self-learning neural techniques boast the ability to solve hard, ill-defined problems and, hence, offer promise for such an application. This paper examines the suitability of four well-known unsupervised techniques for the task of edge linking, by applying them to a test bed of edge point images and then evaluating their performance. Techniques studied are the elastic net, active contours (‘snakes’), Kohonen map and Burr’s modified elastic net. Of these, only the elastic net and the Kohonen map are realistic contenders for general edgelinking tasks, although special treatment of noise in the image is required.