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» On the design of robust classifiers for computer vision
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ICPR
2006
IEEE
14 years 8 months ago
Domain Based LDA and QDA
We propose an alternative to probability density classifiers based on normal distributions LDA and QDA. Instead of estimating covariance matrices using the standard maximum likeli...
David M. J. Tax, Piotr Juszczak, Robert P. W. Duin...
BMVC
1998
13 years 8 months ago
Improving the Robustness of Cell Nucleus Segmentation
A highly successful active contour implementation, for the automatic segmentation of cervical cell nuclei, is shown to lend itself well to a framework that further increases its s...
Pascal Bamford, Brian C. Lovell
ICPR
2010
IEEE
14 years 1 months ago
Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios
Shadow detection and removal is an important step employed after foreground detection, in order to improve the segmentation of objects for tracking. Methods reported in the litera...
Andres Sanin, Conrad Sanderson, Brian Carrington L...
CVPR
2008
IEEE
14 years 1 months ago
A robust descriptor based on Weber's Law
Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, Weber Local Descriptor (WLD). It is based on the fact that human percept...
Jie Chen, Shiguang Shan, Guoying Zhao, Xilin Chen,...
ICCV
2007
IEEE
14 years 9 months ago
Enabling Users to Guide the Design of Robust Model Fitting Algorithms
Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge in model fitting is...
Matthias Wimmer, Freek Stulp, Bernd Radig