Abstract
In present paper, we describe completely innovation architecture of artificial neural nets based on Hopfield structure for solving of stereo matching problem. Hybrid neural network consists of classical analogue Hopfield neural network and maximal neural network. The role of analogue Hopfield network is to find of attraction area of global minimum, whereas maximum network is to find accurate location of this minimum. Presented network characterizes by extremely high rate of working with the same accuracy as classical Hopfield-like network. It is very important as far as application and system of visually impaired people supporting are concerned. Considered network was taken under experimental tests with using real stereo pictures as well simulated stereo images. This allows on calculation of errors and direct comparison to classic analogue Hopfield neural network. Results of tests have shown, that the same accuracy of solution as for continuous Hopfield-like network, can be reached by described here structure in half number of classical Hopfield net iteration.
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References
Aleksander, I.: Artificial vision for robots. Korgan Page (1983)
Barlow, H.B., Blackmore, C., Pettigrew, J.D.: The natural mechanism of binocular depth discrimination. J. Physiology 193, 327–342 (1967)
Faugeras, O.: Three-dimensional computer vision. A geometric viewpoint. MIT Press, Cambridge (1993)
Alvarez, L.: Dense Disparity Map Estimation Respecting Image Discontinuities: A PDE and Scale-Space Based Approach. Journal of Visual Communication and Image Representation 13, 3–21 (2002)
Hopfield, J.J., Tank, D.W.: Neural computation of decisions in optimization problems. Biological Cybernetics 52, 141–152 (1985)
Hopfield, J.J., Tank, D.W.: Artificial neural networks. IEEE Circuits and Devices Magazine 8, 3–10 (1988)
Amit, D.J., Gutfreung, H., Sampolinsky, H.: Spin-glass models of neural networks. Physical Review A2 32(2), 1007–1018 (1985)
Takefuji, Y., Lee, K.C., Aiso, H.: An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain. Biological Cybernetics 67(3), 243–251 (1992)
Takefuji, Y., Lee, K.C.: Neural network computing for knight’s tour problems. Neurocomputing 4, 249–254 (1992)
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Laskowski, Ł. (2010). Hybrid-Maximum Neural Network for Depth Analysis from Stereo-Image. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_7
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DOI: https://doi.org/10.1007/978-3-642-13232-2_7
Publisher Name: Springer, Berlin, Heidelberg
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