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ISM
2006
IEEE
277views Multimedia» more  ISM 2006»
13 years 7 months ago
Efficient Bottom-Up Image Segmentation Using Region Competition and the Mumford-Shah Model for Color and Textured Images
Curve evolution implementations [3][23] [25] of the Mumford-Shah functional [16] are of broad interest in image segmentation. These implementations, however, have initialization p...
Yongsheng Pan, J. Douglas Birdwell, Seddik M. Djou...

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1894views
14 years 2 months ago
Supervised Image Segmentation Using Markov Random Fields
This is the sample implementation of a Markov random field based image segmentation algorithm described in the following papers: 1. Mark Berthod, Zoltan Kato, Shan Yu, and Josi...
Csaba Gradwohl, Zoltan Kato
ICIP
2007
IEEE
13 years 7 months ago
Statistical Multiscale Image Segmentation via Alpha-Stable Modeling
This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by Symmetric Alpha-Stable (SαS) distributions. These features are ef...
Tao Wan, Cedric Nishan Canagarajah, Alin Achim
PAMI
2010
396views more  PAMI 2010»
13 years 5 months ago
Self-Validated Labeling of Markov Random Fields for Image Segmentation
—This paper addresses the problem of self-validated labeling of Markov random fields (MRFs), namely to optimize an MRF with unknown number of labels. We present graduated graph c...
Wei Feng, Jiaya Jia, Zhi-Qiang Liu
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, ...