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ICCV
2007
IEEE
15 years 3 months ago
Co-Tracking Using Semi-Supervised Support Vector Machines
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
ICPR
2008
IEEE
15 years 4 months ago
A new HMM for on-line character recognition using pen-direction and pen-coordinate features
A new hidden Markov model (HMM) is proposed for on-line character recognition using two typical features, pen-direction feature and pen-coordinate feature. These two features are ...
Yoshinori Katayama, Seiichi Uchida, Hiroaki Sakoe
TNN
2008
119views more  TNN 2008»
14 years 9 months ago
Selecting Useful Groups of Features in a Connectionist Framework
Abstract--Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, ther...
Debrup Chakraborty, Nikhil R. Pal
ENC
2004
IEEE
15 years 1 months ago
Feature Selection for Visual Gesture Recognition Using Hidden Markov Models
Hidden Markov models have become the preferred technique for visual recognition of human gestures. However, the recognition rate depends on the set of visual features used, and al...
José Antonio Montero, Luis Enrique Sucar
ICDAR
2009
IEEE
14 years 7 months ago
Combining Multiple HMMs Using On-line and Off-line Features for Off-line Arabic Handwriting Recognition
This paper presents an off-line Arabic Handwriting recognition system based on the selection of different state of the art features and the combination of multiple Hidden Markov M...
Mahdi Hamdani, Haikal El Abed, Monji Kherallah, Ad...