Abstract
There are so many shortcomings in current stereo matching algorithms, for example, they have a low robustness, so as to be influenced by the environment easily, especially the intensity of the light and the number of the occlusion areas; also they often have a poor practical performance for they are difficult to deal with the matching problem without knowing the disparity range and have a high complexity when using the global optimization. In order to solve the above problems, here design a new stereo matching algorithm called RBFSM which main uses the RBF neural network (RBFNN). The RBFSM will get the correspondence between the input layer nodes and hidden layer nodes by the Gaussian function and then use the weight matrix between the hidden layer and output layer to calculate input pixels’ disparity. Here will give the analysis of this new RBF neural network matching algorithm through a lot of experiments, and results show that the new algorithm not only overcome the shortcomings of the traditional methods like low robustness and low practical performance, but also can improve the matching precision significantly with a low complexity.
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© 2011 Springer-Verlag Berlin Heidelberg
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Xu, S., Ye, N., Zhu, F., Xu, S., Zhou, L. (2011). Research on a RBF Neural Network in Stereo Matching. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_31
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DOI: https://doi.org/10.1007/978-3-642-24965-5_31
Publisher Name: Springer, Berlin, Heidelberg
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