Hopfield network for stereo vision correspondence

Abstract

An optimization approach is used to solve the correspondence problem for a set of features extracted from a pair of stereo images. A cost function is defined to represent the constraints on the solution, which is then mapped onto a two-dimensional Hopfield neural network for minimization. Each neuron in the network represents a possible match between a feature in the left image and one in the right image. Correspondence is achieved by initializing (exciting) each neuron that represents a possible match and then allowing the network to settle down into a stable state. The network uses the initial inputs and the compatibility measures between the matched points to find a stable state.