Competitive Hopfield neural network for stereo vision correspondence Export

@inproceedings{citeulike:2194298, abstract = {For the correspondence problem in stereo vision, we developed an Hopfield algorithm that favors unicity of the matches for all interest points both on the left and right images. Although the convergence of the network used cannot be theoretically proven, we have experimentally shown that for the cases we are interested in the method converges either to a stable state or to an acceptable limit cycle. The method is computationally fast}, author = {Mayoral, R. and Perez-Ilzarbe, M. J.}, booktitle = {Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on}, citeulike-article-id = {2194298}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/IJCNN.2000.861513}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=861513}, doi = {10.1109/IJCNN.2000.861513}, journal = {Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on}, keywords = {ann, computervision, neuralnetworks, stereo-vision, stereovision}, pages = {464--469 vol.5}, posted-at = {2008-01-04 11:16:46}, priority = {0}, title = {Competitive Hopfield neural network for stereo vision correspondence}, url = {http://dx.doi.org/10.1109/IJCNN.2000.861513}, volume = {5}, year = {2000} }

TY - CONF ID - citeulike:2194298 L3 - citeulike-article-id:2194298 N2 - For the correspondence problem in stereo vision, we developed an Hopfield algorithm that favors unicity of the matches for all interest points both on the left and right images. Although the convergence of the network used cannot be theoretically proven, we have experimentally shown that for the cases we are interested in the method converges either to a stable state or to an acceptable limit cycle. The method is computationally fast JF - Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on EP - 469 vol.5 TI - Competitive Hopfield neural network for stereo vision correspondence VL - 5 T2 - Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on SP - 464 KW - ann KW - computervision KW - neuralnetworks KW - stereo-vision KW - stereovision AU - Mayoral, R AU - Perez-Ilzarbe, MJ PY - 2000/// UR - http://dx.doi.org/10.1109/IJCNN.2000.861513 ER -