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» A New Bayesian Framework for Object Recognition
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CVPR
2004
IEEE
14 years 9 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
FGR
2008
IEEE
198views Biometrics» more  FGR 2008»
14 years 1 months ago
Recognizing hand gestures using dynamic Bayesian network
In this paper, we describe a dynamic Bayesian network or DBN based approach to both two-hand gestures and onehand gestures. Unlike wired glove-based approaches, the success of cam...
Heung-Il Suk, Bong-Kee Sin, Seong-Whan Lee
CVPR
2007
IEEE
14 years 9 months ago
Objects in Action: An Approach for Combining Action Understanding and Object Perception
Analysis of videos of human-object interactions involves understanding human movements, locating and recognizing objects and observing the effects of human movements on those obje...
Abhinav Gupta, Larry S. Davis
CVPR
2001
IEEE
14 years 9 months ago
A New 3-D Pattern Recognition Technique With Application to Computer Aided Colonoscopy
To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3-D image data need to be constructed and subsequently used by a classificati...
Salih Burak Göktürk, Carlo Tomasi
PAMI
2002
150views more  PAMI 2002»
13 years 6 months ago
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
Decomposing video frames into coherent two-dimensional motion layers is a powerful method for representing videos. Such a representation provides an intermediate description that e...
Hai Tao, Harpreet S. Sawhney, Rakesh Kumar