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» Likelihood Map Fusion for Visual Object Tracking
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CVPR
2003
IEEE
14 years 9 months ago
Variational Inference for Visual Tracking
The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or nonGaussian functions, leading to analytically intractable inference. ...
Jaco Vermaak, Neil D. Lawrence, Patrick Pér...
WSCG
2004
232views more  WSCG 2004»
13 years 9 months ago
Robust Tracking of Athletes Using Multiple Features of Multiple Views
This paper presents a robust and reconfigurable object tracker that integrates multiple visual features from multiple views. The tandem modular architecture stepwise refines the e...
Toshihiko Misu, Seiichi Gohshi, Yoshinori Izumi, Y...
3DIM
2003
IEEE
14 years 26 days ago
Silhouette and Stereo Fusion for 3D Object Modeling
In this paper, we present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both t...
Carlos Hernández Esteban, Francis Schmitt
CORR
2010
Springer
184views Education» more  CORR 2010»
13 years 7 months ago
Image Pixel Fusion for Human Face Recognition
In this paper we present a technique for fusion of optical and thermal face images based on image pixel fusion approach. Out of several factors, which affect face recognition perfo...
Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Mita...
MVA
2007
179views Computer Vision» more  MVA 2007»
13 years 7 months ago
Multi-object trajectory tracking
The majority of existing tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework using a Hidden Markov Model, where the distribution ...
Mei Han, Wei Xu, Hai Tao, Yihong Gong