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SIAMIS
2010
378views more  SIAMIS 2010»
8 years 8 months ago
Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness
Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to ...
Sebastian Nowozin, Christoph H. Lampert
CVPR
2009
IEEE
10 years 8 months ago
Global Connectivity Potentials for Random Field Models
Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions a...
Sebastian Nowozin, Christoph H. Lampert
CVPR
2011
IEEE
8 years 9 months ago
Using Global Bag of Features Models in Random Fields for Joint Categorization and Segmentation of Objects
We propose to bridge the gap between Random Field (RF) formulations for joint categorization and segmentation (JCaS), which model local interactions among pixels and superpixels, ...
Dheeraj Singaraju, René, Vidal
JCB
2006
215views more  JCB 2006»
9 years 1 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...
EMMCVPR
2001
Springer
9 years 5 months ago
A Hierarchical Markov Random Field Model for Figure-Ground Segregation
To segregate overlapping objects into depth layers requires the integration of local occlusion cues distributed over the entire image into a global percept. We propose to model thi...
Stella X. Yu, Tai Sing Lee, Takeo Kanade
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