Proceedings of the Annual Meeting of the Cognitive Science Society

This paper describes a neural network for the detection and representation of oriented edges. Itwas motivated both by the inherent ambiguity of convolution-style edge operators, and the processingof oriented edge information in biological vision systems.The input to the network is the output of oriented edge operators. The computations within thenetwork are based on orientation dependent, three-dimensional, excitatory and inhibitory neighborhoodsin which computations such as lateral inhibition and linear excitation can occur.Rho-space has a variety of interesting properties, which have been investigated. These include:l) Both coarse and fine representation of the orientation information is possible.2) No global thresholding is required, and the local adaptive thresholding is localized in orientation, aswell as in spatial position.3) The filling-in of dotted and dashed lines readily occurs.4) There is a natural representation of connectivity, which agrees with human perception.5) Illusory contours, of one type produced by the human visual system are produced.6) All processing is completely data-driven, and no domain dependent knowledge or model based processingis used.