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» Markov Random Field Models in Computer Vision
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ACIVS
2007
Springer
15 years 5 months ago
A Multi-agent Approach for Range Image Segmentation with Bayesian Edge Regularization
Abstract. We present and evaluate in this paper a multi-agent approach for range image segmentation. The approach consists in using autonomous agents for the segmentation of a rang...
Smaine Mazouzi, Zahia Guessoum, Fabien Michel, Moh...

Publication
222views
17 years 1 months ago
Quantitative Description of Spatially Homogeneous Textures by Characteristic Grey Level Co-Occurrences
Gibbs random eld model with multiple pairwise pixel interactions describes each type of spatially homogeneous image textures in terms of a pixel neighbourhood and Gibbs potentials...
Georgy Gimel'farb
ICPR
2010
IEEE
15 years 4 months ago
Near-Regular BTF Texture Model
—In this paper we present a method for seamless enlargement and editing of intricate near-regular type of bidirectional texture function (BTF) which contains simultaneously both ...
Michael Haindl, Martin Hatka
BMVC
2002
15 years 4 months ago
Randomized RANSAC with T(d, d) test
Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant proportion of outliers. The RANSAC al...
Jiri Matas, Ondrej Chum
ICASSP
2011
IEEE
14 years 5 months ago
EM-style optimization of hidden conditional random fields for grapheme-to-phoneme conversion
We have recently proposed an EM-style algorithm to optimize log-linear models with hidden variables. In this paper, we use this algorithm to optimize a hidden conditional random ï...
Georg Heigold, Stefan Hahn, Patrick Lehnen, Herman...