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CVPR
2004
IEEE
14 years 5 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...
ACCV
2007
Springer
13 years 7 months ago
Motion Observability Analysis of the Simplified Color Correlogram for Visual Tracking
Abstract. Compared with the color histogram, where the position information of each pixel is ignored, a simplified color correlogram (SCC) representation encodes the spatial inform...
Qi Zhao, Hai Tao
ICCV
2007
IEEE
13 years 5 months ago
Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering
Even though sensor fusion techniques based on particle filters have been applied to object tracking, their implementations have been limited to combining measurements from multip...
Bohyung Han, Seong-Wook Joo, Larry S. Davis
ICIP
2008
IEEE
14 years 5 months ago
Kernel-based high-dimensional histogram estimation for visual tracking
We propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters suc...
Allen Tannenbaum, James G. Malcolm, Peter Karasev
JMLR
2010
147views more  JMLR 2010»
12 years 10 months ago
Image Denoising with Kernels Based on Natural Image Relations
A successful class of image denoising methods is based on Bayesian approaches working in wavelet representations. The performance of these methods improves when relations among th...
Valero Laparra, Juan Gutierrez, Gustavo Camps-Vall...