Hopfield-neural-network-based stereo disparity through parallel computing
Daniel P. R. Viegas · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Stereopsis consists of the recovery of three-dimensional scenes from two-dimensional images. Major steps in stereopsis are pre-processing, establishing correspondences and recovering depth. Stereo correspondence is the weightiest of concern. A tremendously flexible environment exists compounding the problem. Hopfield neural networks particularly offers promise, as it is fully inter-connected, converges to a solution and is recurrent. Parallel computing becomes an absolute ingredient due to the enormity of computation. This paper utilizes the matching primitive of edge pixel features with optimization as the relaxation method and a focus on parallel implementation. The computational structure consists of layers, epilayer lines and separate orientations. Similarity, smoothness, uniqueness and ordering are the matching constraints. Energy minimization incorporates all the constraints by exciting and inhibiting neurons. Results are obtained from a number of real color images of differing complexity. The computational intelligence is displayed in terms of efficiency and speed- up. Efficiency, by way of a matching function covering color images and the incorporation of all the constraints into the neural network. Speed-up is demonstrated in the parallel implementation. Stereo disparity is thus obtained with only a few mismatches and a sharp step-down from an initial duration of forty hours to a mere nine minutes.