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» Markov Random Field Models in Computer Vision
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ACIVS
2007
Springer
15 years 2 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
16 years 9 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 11 days 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 10 days 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 1 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...