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BMCBI
2007
160views more  BMCBI 2007»
13 years 8 months ago
Identifying protein complexes directly from high-throughput TAP data with Markov random fields
Background: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms ...
Wasinee Rungsarityotin, Roland Krause, Arno Sch&ou...
JCB
2006
215views more  JCB 2006»
13 years 8 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...
PAMI
2008
198views more  PAMI 2008»
13 years 8 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation....
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...
PAMI
2010
215views more  PAMI 2010»
13 years 7 months ago
Fusion Moves for Markov Random Field Optimization
—The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one p...
Victor S. Lempitsky, Carsten Rother, Stefan Roth, ...
ICPR
2008
IEEE
14 years 3 months ago
Combining shape-from-shading and stereo using Gaussian-Markov random fields
In this paper we present a method of combining stereo and shape-from-shading information, taking account of the local reliability of each shape estimate. Local estimates of dispar...
Tom S. F. Haines, Richard C. Wilson