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» Supervised Image Segmentation Using Markov Random Fields
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TMM
2002
104views more  TMM 2002»
14 years 11 months ago
Spatial contextual classification and prediction models for mining geospatial data
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for i...
Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsa...
IPMI
2005
Springer
16 years 18 days ago
From Spatial Regularization to Anatomical Priors in fMRI Analysis
In this paper, we study Markov Random Fields as spatial smoothing priors in fMRI detection. Relatively high noise in fMRI images presents a serious challenge for the detection algo...
Wanmei Ou, Polina Golland
ICIP
1998
IEEE
16 years 1 months ago
Lip Features Automatic Extraction
An algorithm for speaker's lip segmentation and features extraction is presented in this paper. A color video sequence of speaker's face is acquired, under natural light...
Franck Luthon, Marc Liévin
105
Voted
CVPR
2009
IEEE
16 years 6 months ago
Efficient Scale Space Auto-Context for Image Segmentation and Labeling
The Conditional Random Fields (CRF) model, using patch-based classification bound with context information, has recently been widely adopted for image segmentation/ labeling. In...
Jiayan Jiang (UCLA), Zhuowen Tu (UCLA)
MVA
1990
123views Computer Vision» more  MVA 1990»
15 years 28 days ago
Randomized Hough Transform (RHT) in Engineering Drawing Vectorization System
Abstract When the data is processed from the digitized drawThis paper presents how the recently presented Randomized IIough Transform (RHT) method can be used as a part of an engin...
Pekka Kultanen, Erkki Oja, Lei Xu