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» Discrete Mixture Models for Unsupervised Image Segmentation
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PAMI
2002
108views more  PAMI 2002»
13 years 8 months ago
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hid...
Derek C. Stanford, Adrian E. Raftery
ECCV
2000
Springer
14 years 10 months ago
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
Abstract. This paper presents anovel variational method forimage segmentation that uni es boundary and region-based information sources under the Geodesic Active Region framework. ...
Nikos Paragios, Rachid Deriche
CGF
2008
148views more  CGF 2008»
13 years 9 months ago
Automatic Registration for Articulated Shapes
We present an unsupervised algorithm for aligning a pair of shapes in the presence of significant articulated motion and missing data, while assuming no knowledge of a template, u...
Will Chang, Matthias Zwicker
ACCV
2007
Springer
14 years 3 months ago
Image Segmentation Using Co-EM Strategy
Inspired by the idea of multi-view, we proposed an image segmentation algorithm using co-EM strategy in this paper. Image data are modeled using Gaussian Mixture Model (GMM), and t...
Zhenglong Li, Jian Cheng, Qingshan Liu, Hanqing Lu
IVC
2008
141views more  IVC 2008»
13 years 8 months ago
Segmentation of color images via reversible jump MCMC sampling
Reversible jump Markov chain Monte Carlo (RJMCMC) is a recent method which makes it possible to construct reversible Markov chain samplers that jump between parameter subspaces of...
Zoltan Kato