A generative perspective on MRFs in low-level vision

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 1751-1758

  Schmidt, Uwe ^a   Gao, Qi ^a   Roth, Stefan ^a  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION-SPECIFIC MODEL; BAYESIAN; COMMON MODELS; DISCRIMINATIVE METHODS; GENERAL CLASS; GENERATIVE MODEL; GIBBS SAMPLERS; IMAGE RESTORATION; LOW-LEVEL VISION; MARKOV RANDOM FIELD; MAXIMUM A POSTERIORI ESTIMATION; MINIMUM MEAN SQUARED ERROR; MMSE ESTIMATION; NATURAL IMAGES; NON-PROBABILISTIC; PRIOR KNOWLEDGE; PROBABILISTIC MODELS; SAMPLING-BASED;

COMPUTER VISION; IMAGE RECONSTRUCTION; MEAN SQUARE ERROR;

SAMPLING;

EID: 77955989583     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539844     Document Type: Conference Paper

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