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CVPR
2007
IEEE
14 years 11 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen
CVPR
2007
IEEE
14 years 11 months ago
Learning Conditional Random Fields for Stereo
State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for e...
Daniel Scharstein, Chris Pal
CVPR
2003
IEEE
14 years 11 months ago
Video Segmentation Based on Graphical Models
This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field...
Kia-Fock Loe, Tele Tan, Yang Wang 0002
EMNLP
2006
13 years 11 months ago
A Skip-Chain Conditional Random Field for Ranking Meeting Utterances by Importance
We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies bet...
Michel Galley
EMNLP
2006
13 years 11 months ago
A Hybrid Markov/Semi-Markov Conditional Random Field for Sequence Segmentation
Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmentation and labeling. Both models have advantages in terms of the typ...
Galen Andrew