Abstract
In this paper, we propose a method for reconstructing the surfaces of objects from stereo data. The proposed method quantitatively defines not only the fitness of the stereo data to surfaces but also the connectivity and smoothness of the surfaces in the framework of a three-dimensional (3-D) Markov Random Field (MRF) model. The surface reconstruction is accomplished by searching for the most possible MRF’s state. Experimental results are shown for artificial and actual stereo data.
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Takizawa, H., Yamamoto, S. (2005). Surface Reconstruction from Stereo Data Using Three-Dimensional Markov Random Field Model. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_49
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DOI: https://doi.org/10.1007/11552499_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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