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BMVC
2001
15 years 3 days ago
Graph Matching using Adjacency Matrix Markov Chains
This paper describes a spectral method for graph-matching. We adopt a graphical models viewpoint in which the graph adjacency matrix is taken to represent the transition probabili...
Antonio Robles-Kelly, Edwin R. Hancock
100
Voted
DICTA
2003
14 years 11 months ago
Gesture Classification Using Hidden Markov Models and Viterbi Path Counting
Human-Machine interfaces play a role of growing importance as computer technology continues to evolve. Motivated by the desire to provide users with an intuitive gesture input syst...
Nianjun Liu, Brian C. Lovell
ICMCS
2010
IEEE
193views Multimedia» more  ICMCS 2010»
14 years 10 months ago
Motion segmentation in compressed video using Markov Random Fields
In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First,...
Yue-Meng Chen, Ivan V. Bajic, Parvaneh Saeedi
WACV
2005
IEEE
15 years 3 months ago
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
This paper addresses the problem of tracking human body pose in monocular video including automatic pose initialization and re-initialization after tracking failures caused by par...
Mun Wai Lee, Ramakant Nevatia
94
Voted
NIPS
2004
14 years 11 months ago
Semi-Markov Conditional Random Fields for Information Extraction
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semiCRF on an input sequence x outputs a "...
Sunita Sarawagi, William W. Cohen