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» Graph model selection using maximum likelihood
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AMFG
2003
IEEE
144views Biometrics» more  AMFG 2003»
14 years 24 days ago
Boosted Audio-Visual HMM for Speech Reading
We propose a new approach for combining acoustic and visual measurements to aid in recognizing lip shapes of a person speaking. Our method relies on computing the maximum likeliho...
Pei Yin, Irfan A. Essa, James M. Rehg
JAMDS
2002
107views more  JAMDS 2002»
13 years 7 months ago
Estimating a resource selection function with line transect sampling
Abstract. A resource selection probability function is a function that gives the probability that a resource unit (e.g., a plot of land) that is described by a set of habitat varia...
Bryan F. J. Manly
ICCCN
2007
IEEE
13 years 7 months ago
Online Selection of Tracking Features using AdaBoost
In this paper, a novel feature selection algorithm for object tracking is proposed. This algorithm performs more robust than the previous works by taking the correlation between f...
Ying-Jia Yeh, Chiou-Ting Hsu
PR
2006
89views more  PR 2006»
13 years 7 months ago
Gaussian fields for semi-supervised regression and correspondence learning
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
Jakob J. Verbeek, Nikos A. Vlassis
ISCI
2007
170views more  ISCI 2007»
13 years 7 months ago
Automatic learning of cost functions for graph edit distance
Graph matching and graph edit distance have become important tools in structural pattern recognition. The graph edit distance concept allows us to measure the structural similarit...
Michel Neuhaus, Horst Bunke