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» Robust Computer Vision through Kernel Density Estimation
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ECCV
2004
Springer
14 years 9 months ago
Image and Video Segmentation by Anisotropic Kernel Mean Shift
Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kerne...
Jue Wang, Bo Thiesson, Yingqing Xu, Michael F. Coh...
ICPR
2006
IEEE
14 years 8 months ago
Adaptive Feature Integration for Segmentation of 3D Data by Unsupervised Density Estimation
In this paper, a novel unsupervised approach for the segmentation of unorganized 3D points sets is proposed. The method derives by the mean shift clustering paradigm devoted to se...
Marco Cristani, Umberto Castellani, Vittorio Murin...
ICDCS
2007
IEEE
14 years 2 months ago
Distributed Density Estimation Using Non-parametric Statistics
Learning the underlying model from distributed data is often useful for many distributed systems. In this paper, we study the problem of learning a non-parametric model from distr...
Yusuo Hu, Hua Chen, Jian-Guang Lou, Jiang Li
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...
CVPR
2005
IEEE
14 years 9 months ago
The Modified pbM-Estimator Method and a Runtime Analysis Technique for the RANSAC Family
Robust regression techniques are used today in many computer vision algorithms. Chen and Meer recently presented a new robust regression technique named the projection based M-est...
Stas Rozenfeld, Ilan Shimshoni