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ISBI
2008
IEEE
14 years 2 months ago
Support vector driven Markov random fields towards DTI segmentation of the human skeletal muscle
In this paper we propose a classification-based method towards the segmentation of diffusion tensor images. We use Support Vector Machines to classify diffusion tensors and we ex...
Radhouène Neji, Gilles Fleury, Jean Francoi...
BMCBI
2007
160views more  BMCBI 2007»
13 years 7 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...
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
13 years 11 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
CVPR
2006
IEEE
14 years 9 months ago
AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov r...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh, ...
PAMI
2007
123views more  PAMI 2007»
13 years 7 months ago
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
—Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, ...
Dalila Benboudjema, Wojciech Pieczynski