Stereo correspondence with discrete-time cellular neural networks

In this paper, we propose a new approach of solving the stereopsis problem with a discrete-time cellular neural network (DTCNN) where each node has connections only with its local neighbors. Because the matching process of stereo correspondence depends on its geometrically local characteristics, the DTCNN is suitable for the stereo correspondence. Moreover, it can be easily implemented in VLSI. Therefore, we employed a two-layer DTCNN with dual templates, which are determined with the back propagation learning rule. Based on evaluation of the proposed approach for several random dot stereograms, its performance is better than that of the Marr-Poggio algorithm