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» Feature Correspondence: A Markov Chain Monte Carlo Approach
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SSPR
2004
Springer
14 years 25 days ago
An MCMC Feature Selection Technique for Characterizing and Classifying Spatial Region Data
We focus on characterizing spatial region data when distinct classes of structural patterns are present. We propose a novel statistical approach based on a supervised framework for...
Despina Kontos, Vasileios Megalooikonomou, Marc J....
TIP
2008
111views more  TIP 2008»
13 years 7 months ago
Unsupervised Bayesian Convex Deconvolution Based on a Field With an Explicit Partition Function
This paper proposes a non-Gaussian Markov field with a special feature: an explicit partition function. To the best of our knowledge, this is an original contribution. Moreover, th...
Jean-François Giovannelli
AAAI
2006
13 years 9 months ago
Unifying Logical and Statistical AI
Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov l...
Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew...
ECCV
2008
Springer
15 years 1 days ago
Co-Recognition of Image Pairs by Data-Driven Monte Carlo Image Exploration
We introduce a new concept of co-recognition for object-level image matching between an arbitrary image pair. Our method augments putative local regionmatches to reliable object-...
Minsu Cho (Seoul National University), Young Min S...
ICIP
2005
IEEE
14 years 9 months ago
Tracking multiple cells by correspondence resolution in a sequential Bayesian framework
We propose a multi-target tracking (MTT) algorithm in a sequential Bayesian framework that computes cell velocities from video microscopy. Unlike the traditional tracking methods,...
Nilanjan Ray, Gang Dong, Scott T. Acton