Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields.
Tomás CrivelliBruno Cernuschi-FríasPatrick BouthemyJian-Feng YaoPublished in: SIAM J. Imaging Sci. (2013)
Keyphrases
- markov random field
- textured images
- image segmentation
- energy function
- mrf model
- markov random field model
- discriminative random fields
- low level vision
- graph cuts
- parameter estimation
- texture segmentation
- unsupervised segmentation
- random fields
- belief propagation
- image restoration
- iterated conditional modes
- energy minimization
- maximum a posteriori
- mrf models
- scene labeling
- higher order
- pairwise
- label field
- level set
- segmentation method
- texture model
- color images
- image sequences
- optical flow
- natural images
- min cut
- image processing
- map estimation
- markov networks
- model selection
- maximum likelihood
- motion estimation
- image analysis
- machine learning
- texture information
- stereo matching
- texture features
- map inference
- markov fields
- object recognition
- multiscale