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» 2D Shape Recognition by Hidden Markov Models
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CVPR
2005
IEEE
16 years 8 months ago
Learning to Estimate Human Pose with Data Driven Belief Propagation
We propose a statistical formulation for 2-D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to...
Gang Hua, Ming-Hsuan Yang, Ying Wu
PAMI
2002
98views more  PAMI 2002»
15 years 5 months ago
Extraction of Visual Features for Lipreading
The multimodal nature of speech is often ignored in human-computer interaction, but lip deformations and other body motion, such as those of the head, convey additional information...
Iain Matthews, Timothy F. Cootes, J. Andrew Bangha...
FGR
2008
IEEE
134views Biometrics» more  FGR 2008»
16 years 18 days ago
HMM parameter reduction for practical gesture recognition
We examine in detail some properties of gesture recognition models which utilize a reduced number of parameters and lower algorithmic complexity compared to traditional hidden Mar...
Stjepan Rajko, Gang Qian
FGR
2000
IEEE
162views Biometrics» more  FGR 2000»
15 years 9 months ago
Person Tracking in Real-World Scenarios Using Statistical Methods
This paper presents a novel approach to robust and flexible person tracking using an algorithm that combines two powerful stochastic modeling techniques: The first one is the tech...
Gerhard Rigoll, Stefan Eickeler, Stefan Mülle...
ECCV
2006
Springer
16 years 8 months ago
An Integrated Model for Accurate Shape Alignment
In this paper, we propose a two-level integrated model for accurate face shape alignment. At the low level, the shape is split into a set of line segments which serve as the nodes ...
Lin Liang, Fang Wen, Xiaoou Tang, Ying-Qing Xu