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» Markov Random Field Modeling in Computer Vision
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ISVC
2007
Springer
14 years 3 months ago
A Vision-Based Architecture for Intent Recognition
Abstract. Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly rel...
Alireza Tavakkoli, Richard Kelley, Christopher Kin...
JMLR
2010
191views more  JMLR 2010»
13 years 4 months ago
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some...
Michael Gutmann, Aapo Hyvärinen
ICASSP
2010
IEEE
13 years 4 months ago
Unsupervised knowledge acquisition for Extracting Named Entities from speech
This paper presents a Named Entity Recognition (NER) method dedicated to process speech transcriptions. The main principle behind this method is to collect in an unsupervised way ...
Frédéric Béchet, Eric Charton
ICIP
2006
IEEE
14 years 11 months ago
Background Modeling from GMM Likelihood Combined with Spatial and Color Coherency
This paper proposes to combine spatial and color coherency with the pixel-wise GMM to determine the background model. We first represent each pixel with a hybrid feature vector, w...
Sheng-Yan Yang, Chiou-Ting Hsu
UAI
2004
13 years 10 months ago
Case-Factor Diagrams for Structured Probabilistic Modeling
We introduce a probabilistic formalism subsuming Markov random fields of bounded tree width and probabilistic context free grammars. Our models are based on a representation of Bo...
David A. McAllester, Michael Collins, Fernando Per...