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» Improving DPOP with function filtering
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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...
ICDE
2008
IEEE
152views Database» more  ICDE 2008»
14 years 8 months ago
Efficient Merging and Filtering Algorithms for Approximate String Searches
We study the following problem: how to efficiently find in a collection of strings those similar to a given query string? Various similarity functions can be used, such as edit dis...
Chen Li, Jiaheng Lu, Yiming Lu
ICIP
2007
IEEE
14 years 9 months ago
Multi-Modal Particle Filtering Tracking using Appearance, Motion and Audio Likelihoods
We propose a multi-modal object tracking algorithm that combines appearance, motion and audio information in a particle filter. The proposed tracker fuses at the likelihood level ...
Matteo Bregonzio, Murtaza Taj, Andrea Cavallaro
NIPS
2003
13 years 8 months ago
Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression
Nonlinear filtering can solve very complex problems, but typically involve very time consuming calculations. Here we show that for filters that are constructed as a RBF network ...
Roland Vollgraf, Michael Scholz, Ian A. Meinertzha...
ISBI
2008
IEEE
14 years 8 months ago
Improved fMRI group studies based on spatially varying non-parametric BOLD signal modeling
Multi-subject analysis of functional Magnetic Resonance Imaging (fMRI) data relies on within-subject studies, which are usually conducted using a massively univariate approach. In...
Philippe Ciuciu, Thomas Vincent, Anne-Laure Fouque...