Stereo-correspondence using Gabor logons and neural networks

Stereo-correspondence is the most important issue in stereopsis. Feature extraction and matching are the basic steps involved in the solution of the stereo-correspondence problem. The article examines the effectiveness of Gabor logons as a pre-processing technique compared to the intensity image. The matching is performed using a Hopfield network and simulated annealing. The performance of these matching techniques with respect to their accuracy and execution speed is analysed. The effect of weightages to constraints and network parameters is also analysed. Simulated annealing is found to give much faster convergence compared to the Hopfield network.