Abstract
In this paper, RBF network (RBFN) is used to provide effective methodologies for solving difficult computational problems in camera calibration and 3D reconstruction process. RBFN works in three aspects: Firstly, a RBFN is adopted to learn and memorize the nonlinear relationship in stereovision system. Secondly, another RBFN is trained to search the correspondent lines in two images such that stereo matching is performed in one dimension. Finally, the trained network in the first stage is used to reconstruct the object’s 3D figuration and surface. The technique avoids the complicated and large calculation in conventional methods. Experiments have been performed on common stereo pairs and the results are accurate and convincing.
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Hu, Hf. (2006). Camera Calibration and 3D Reconstruction Using RBF Network in Stereovision System. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_55
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DOI: https://doi.org/10.1007/11760023_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
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