A new approach for stereo reconstruction is proposed. This approach is based on a Gibbs probability distribution for surfaces in 3D space. The problem of stereo reconstruction is formulated then as a Bayes decision task. The main difference compared with known methods is the use of a more realistic cost function. In case of stereo reconstruction this function can be designed in some natural way, taking into account the properties of the surface model used. The proposed method solves the Bayes decision task approximately by a Gibbs Sampler. Learning of unknown distribution parameters is included as well, using the Expectation Maximization algorithm. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Schlesinger, D. (2003). Gibbs probability distributions for stereo reconstruction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2781, 394–401. https://doi.org/10.1007/978-3-540-45243-0_51
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