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187
Voted
CVPR
2009
IEEE
17 years 2 months ago
Let the Kernel Figure it Out; Principled Learning of Pre-processing for Kernel Classifiers
Most modern computer vision systems for high-level tasks, such as image classification, object recognition and segmentation, are based on learning algorithms that are able to se...
Peter V. Gehler, Sebastian Nowozin
CVPR
1998
IEEE
16 years 9 months ago
A Methodology for Deriving Probabilistic Correctness Measures from Recognizers
This paper describes the derivation of probability of correctness from scores assigned by most recognizers. Motivation for this research is three-fold: i probability values can be...
Djamel Bouchaffra, Venu Govindaraju, Sargur N. Sri...
CVPR
2001
IEEE
16 years 9 months ago
A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition
The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognit...
J. Ross Beveridge, Kai She, Bruce A. Draper, Geof ...
CVPR
2006
IEEE
16 years 9 months ago
Incorporating the Boltzmann Prior in Object Detection Using SVM
In this paper we discuss object detection when only a small number of training examples are given. Specifically, we show how to incorporate a simple prior on the distribution of n...
Margarita Osadchy, Daniel Keren
200
Voted
ICCV
2005
IEEE
16 years 8 months ago
Designing Spatially Coherent Minimizing Flows for Variational Problems Based on Active Contours
This paper tackles an important aspect of the variational problems involving active contours, which has been largely overlooked so far: the optimization by gradient flows. Classic...
Guillaume Charpiat, Renaud Keriven, Jean-Philippe ...