Use of 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 2-D 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 all the neurons that represent the possible matches and allowing the network to use the compatibility measures between the matched points to settle down into a stable state.<>