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» What Energy Functions Can Be Minimized via Graph Cuts
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ECCV
2006
Springer
14 years 9 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields
One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with som...
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...
BMVC
2010
13 years 5 months ago
Patch-Cuts: A Graph-Based Image Segmentation Method Using Patch Features and Spatial Relations
In this paper, we present a graph-based image segmentation method (patch-cuts) that incorporates features and spatial relations obtained from image patches. In the first step, pat...
Gerd Brunner, Deepak Roy Chittajallu, Uday Kurkure...
PAKDD
2004
ACM
96views Data Mining» more  PAKDD 2004»
14 years 25 days ago
Spectral Energy Minimization for Semi-supervised Learning
The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-super...
Chun Hung Li, Zhi-Li Wu
CVPR
2004
IEEE
14 years 9 months ago
Spatially Coherent Clustering Using Graph Cuts
Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixe...
Ramin Zabih, Vladimir Kolmogorov
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, ...