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» On Comparing Two Scenarios for Probabilistic Image Modelling
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ICIP
2001
IEEE
14 years 10 months ago
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...
Guorong Xuan, Wei Zhang, Peiqi Chai
JMIV
2007
114views more  JMIV 2007»
13 years 8 months ago
Noisy Image Decomposition: A New Structure, Texture and Noise Model Based on Local Adaptivity
These last few years, image decomposition algorithms have been proposed to split an image into two parts: the structures and the textures. These algorithms are not adapted to the ...
Jérôme Gilles
ICIP
2006
IEEE
14 years 10 months ago
An adaptive mixture color model for robust visual tracking
Global color characterization is a very powerful tool to model in a simple yet discriminant way the visual appearance of complex objects. A fixed reference model of this type can ...
Antoine Lehuger, Patrick Léchat, Patrick P&...
PVLDB
2008
94views more  PVLDB 2008»
13 years 8 months ago
Dynamic active probing of helpdesk databases
Helpdesk databases are used to store past interactions between customers and companies to improve customer service quality. One common scenario of using helpdesk database is to fi...
Shenghuo Zhu, Tao Li, Zhiyuan Chen, Dingding Wang,...
ICPR
2000
IEEE
14 years 2 days ago
MRF Solutions for Probabilistic Optical Flow Formulations
In this paper we propose an efficient, non-iterative method for estimating optical flow. We develop a probabilistic framework that is appropriate for describing the inherent uncer...
Sébastien Roy, Venu Govindu