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» Hidden Markov Support Vector Machines
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ISBI
2008
IEEE
14 years 5 months ago
Support vector driven Markov random fields towards DTI segmentation of the human skeletal muscle
In this paper we propose a classification-based method towards the segmentation of diffusion tensor images. We use Support Vector Machines to classify diffusion tensors and we ex...
Radhouène Neji, Gilles Fleury, Jean Francoi...
CVPR
2007
IEEE
15 years 20 days ago
Latent-Dynamic Discriminative Models for Continuous Gesture Recognition
Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper, we develop a discriminative framework for simultaneous se...
Louis-Philippe Morency, Ariadna Quattoni, Trevor D...
ICMCS
2006
IEEE
128views Multimedia» more  ICMCS 2006»
14 years 4 months ago
Efficient Recognition of Authentic Dynamic Facial Expressions on the Feedtum Database
In order to allow for fast recognition of a user’s affective state we discuss innovative holistic and self organizing approaches for efficient facial expression analysis. The f...
Frank Wallhoff, Björn Schuller, Michael Hawel...
ICML
1999
IEEE
14 years 11 months ago
Abstracting from Robot Sensor Data using Hidden Markov Models
ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003...
Laura Firoiu, Paul R. Cohen
BMCBI
2007
153views more  BMCBI 2007»
13 years 10 months ago
Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis
Background: A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel ...
Matthew Landry, Stephen Winters-Hilt