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» 2D Shape Recognition by Hidden Markov Models
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FLAIRS
2008
15 years 4 months ago
Learning Dynamic Naive Bayesian Classifiers
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Miriam Martínez, Luis Enrique Sucar
109
Voted
CIVR
2009
Springer
257views Image Analysis» more  CIVR 2009»
15 years 8 months ago
Trajectory-based handball video understanding
This paper presents a content-based approach for understanding handball videos. Tracked players are characterized by their 2D trajectories in the court plane. The trajectories and...
Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre L...
111
Voted
IBPRIA
2003
Springer
15 years 7 months ago
Chromosome Classification Using Continuous Hidden Markov Models
Up-to-date results on the application of Markov models to chromosome analysis are presented. On the one hand, this means using continuous Hidden Markov Models (HMMs) instead of dis...
César Martínez, Héctor Garc&i...
TCSV
2008
177views more  TCSV 2008»
15 years 1 months ago
An ICA Mixture Hidden Markov Model for Video Content Analysis
In this paper, a new theoretical framework based on hidden Markov model (HMM) and independent component analysis (ICA) mixture model is presented for content analysis of video, nam...
Jian Zhou, Xiao-Ping Zhang
ICPR
2008
IEEE
15 years 8 months ago
Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models
One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty ...
Djamel Bouchaffra