190 search results - page 9 / 38 » Markov Random Fields with Efficient Approximations |
CVPR
2000
IEEE
14 years 9 months ago
2000
IEEE
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
CVPR
2006
IEEE
14 years 9 months ago
2006
IEEE
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...
ICIP
2010
IEEE
13 years 5 months ago
2010
IEEE
Based on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffus...
Publication
Drawbacks of the traditional scenario of image modeling by Gibbs random fields with multiple pairwise pixel interactions are outlined, and a more reasonable alternative scenario b...
TSP
2008
13 years 7 months ago
2008
Markov random fields are designed to represent structured dependencies among large collections of random variables, and are well-suited to capture the structure of real-world sign...