Representing stereo data with the Delaunay triangulation
Artificial Intelligence
From Images to Surfaces: A Computational Study of the Human Early Visual System
From Images to Surfaces: A Computational Study of the Human Early Visual System
Surface Reconstruction from Feature Based Stereo
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Surface Reconstruction from Stereo Data Using a Three-Dimensional Markov Random Field Model
IEICE - Transactions on Information and Systems
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
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In the present paper, we propose a method for reconstructing the shapes of block-like objects from stereovision data. Flat surfaces and ridge lines are represented by three-dimensional (3-D) discrete object models. Interrelations between the object models are formulated by use of the framework of a 3-D Markov Random Field (MRF) model. The shape reconstruction is accomplished by searching for the most likely state of the MRF model. The searching is performed by the Markov Chain Monte Carlo (MCMC) method. An experimental result is shown for real stereo data.