Stereo vision: a neural network application to constraint satisfaction problem
M.S. Mousavi, Robert J. Schalkoff · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
In this paper, a stereo vision matching algorithm, implemented via a neural network architecture, is described. The stereo matching problem, that is, finding the correspondence of features between two images, can be cast as a constraint satisfaction problem. The algorithm uses image edge features and assumes a parallel-axis camera geometry such that the corresponding image points must lie in the same scanline. Intra-scanline constraints are used to to perform multipleconstraint satisfaction searches for the correct match. Further, inter-scanline constraints are used to enforce consistent matches by eliminating those that are not getting enough support from the neighboring scanlines. The inter-scanline constraints are implemented in a 3-D neural network which is formed by a stack of 2-D neuron layers. First, a mulilayered network is designed to extract the features points for matching using a static neural network. A similarity measure is defined for each pair of feature point matches which are then passed on to the second stage of the algorithm. The purpose of the second stage is to turn the difficult correspondence problem into a constraint satisfaction problem by imposing relational constraints. The result of computer simulations are presented to demonstrate the effectiveness of the approach.