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PRL
2006
153views more  PRL 2006»
13 years 7 months ago
Efficient adaptive density estimation per image pixel for the task of background subtraction
We analyze the computer vision task of pixel-level background subtraction. We present recursive equations that are used to constantly update the parameters of a Gaussian mixture m...
Zoran Zivkovic, Ferdinand van der Heijden
ICIP
2009
IEEE
14 years 8 months ago
Joint Recovery And Segmentation Of Polarimetric Images Using A Compound Mrf And Mixture Modeling
We propose a new approach for the restoration of polarimetric Stokes images, capable of simultaneously segmenting and restoring the images. In order to easily handle the admissibi...
CVPR
2001
IEEE
14 years 9 months ago
A Bayesian Approach to Digital Matting
This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel ...
Yung-Yu Chuang, Brian Curless, David Salesin, Rich...
BMVC
2010
13 years 5 months ago
Background Modelling on Tensor Field for Foreground Segmentation
The paper proposes a new method to perform foreground detection by means of background modeling using the tensor concept. Sometimes, statistical modelling directly on image values...
Rui Caseiro, Jorge Batista, Pedro Martins
MVA
2002
195views Computer Vision» more  MVA 2002»
13 years 7 months ago
Improved Adaptive Mixture Learning for Robust Video Background Modeling
2 Related Works Gaussian mixtures are often used for data modeling in many real-time applications such as video background modeling and speaker direction tracking. The real-time a...
Dar-Shyang Lee