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» A Discriminative Framework for Modelling Object Classes
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AVSS
2006
IEEE
14 years 27 days ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
IVC
2007
188views more  IVC 2007»
13 years 9 months ago
Integration of deformable contours and a multiple hypotheses Fisher color model for robust tracking in varying illuminant enviro
In this paper, we propose a new technique to perform figure-ground segmentation in image sequences of moving objects under varying illumination conditions. Unlike most of the alg...
Francesc Moreno-Noguer, Alberto Sanfeliu, Dimitris...
ECCV
2008
Springer
14 years 11 months ago
Multiple Component Learning for Object Detection
Abstract. Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, a...
Boris Babenko, Pietro Perona, Piotr Dollár,...
WSC
1997
13 years 10 months ago
SimJAVA - A Framework for Modeling Queueing Networks in Java
The paper present a layered design for a discrete event simulation framework based on the Java programming language. A description of this project’s goals and motivation is foll...
Wolfgang Kreutzer, Jane Hopkins, Marcel van Mierlo
CVPR
2006
IEEE
14 years 11 months ago
A Framework for Feature Selection for Background Subtraction
Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveill...
Toufiq Parag, Ahmed M. Elgammal, Anurag Mittal