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ICPR
2008
IEEE
14 years 8 months ago
Change detection based on adaptive Markov Random Fields
Usually changes in remote sensing images go along with the appearance or disappearance of some edges. In addition, pixels located along the edges are likely to weakly influenced b...
Chunlei Huo, Hanqing Lu, Jian Cheng, Keming Chen, ...
ICPR
2006
IEEE
14 years 8 months ago
Detecting Coarticulation in Sign Language using Conditional Random Fields
Coarticulation is one of the important factors that makes automatic sign language recognition a hard problem. Unlike in speech recognition, coarticulation effects in sign language...
Ruiduo Yang, Sudeep Sarkar
ICPR
2002
IEEE
14 years 8 months ago
Illumination Invariant Segmentation of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model
For many years, object tracking in images has suffered from the problems of occlusions and illumination effects. In order to resolve occlusion problems, we have been proposing the...
Shunsuke Kamijo, Katsushi Ikeuchi, Masao Sakauchi
ICML
2007
IEEE
14 years 8 months ago
Dynamic hierarchical Markov random fields and their application to web data extraction
Hierarchical models have been extensively studied in various domains. However, existing models assume fixed model structures or incorporate structural uncertainty generatively. In...
Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen
ICML
2007
IEEE
14 years 8 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...