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PAMI
2008
198views more  PAMI 2008»
13 years 7 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation....
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...
CISIS
2008
IEEE
14 years 2 months ago
Segmentation of the Liver from Abdominal CT Using Markov Random Field Model and GVF Snakes
Liver segmentation from scans of the abdominal area is an important step in several diagnostic processes. CT scans of the abdominal area contain several organs in close proximity ...
Raja' S. Alomari, Suryaprakash Kompalli, Vipin Cha...
NIPS
2003
13 years 9 months ago
Discriminative Fields for Modeling Spatial Dependencies in Natural Images
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial depe...
Sanjiv Kumar, Martial Hebert
ISBI
2006
IEEE
14 years 8 months ago
A tightly coupled region-shape framework for 3D medical image segmentation
Most hybrid 3D segmentation methods either heuristically couple the respective algorithm or combine a true 3D with a 2D algorithm due to computational considerations. In this pape...
Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
CVPR
2008
IEEE
14 years 10 months ago
The Logistic Random Field - A convenient graphical model for learning parameters for MRF-based labeling
Graphical models are fundamental tools for modeling images and other applications. In this paper, we propose the Logistic Random Field (LRF) model for representing a discrete-valu...
Marshall F. Tappen, Kegan G. G. Samuel, Craig V. D...