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» Sequential Kernel Density Approximation and Its Application ...
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PAMI
2008
161views more  PAMI 2008»
13 years 3 months ago
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
CVPR
2004
IEEE
14 years 6 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...
ICIP
2005
IEEE
13 years 10 months ago
Improving particle filter with support vector regression for efficient visual tracking
—Particle filter is a powerful visual tracking tool based on sequential Monte Carlo framework, and it needs large numbers of samples to properly approximate the posterior density...
Guangyu Zhu, Dawei Liang, Yang Liu, Qingming Huang...
TIP
2002
126views more  TIP 2002»
13 years 4 months ago
A generic approach to simultaneous tracking and verification in video
In this paper, a generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte...
Baoxin Li, Rama Chellappa
ECCV
2008
Springer
14 years 6 months ago
Online Sparse Matrix Gaussian Process Regression and Vision Applications
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP ...
Ananth Ranganathan, Ming-Hsuan Yang