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» Preference learning with Gaussian processes
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CVPR
2007
IEEE
14 years 10 months ago
What makes a good model of natural images?
Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non G...
Yair Weiss, William T. Freeman
ICASSP
2011
IEEE
13 years 5 days ago
Denoising sparse noise via online dictionary learning
The idea of learning overcomplete dictionaries based on the paradigm of compressive sensing has found numerous applications, among which image denoising is considered one of the m...
Anoop Cherian, Suvrit Sra, Nikolaos Papanikolopoul...
RAS
2010
216views more  RAS 2010»
13 years 6 months ago
A nonparametric learning approach to range sensing from omnidirectional vision
We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available dur...
Christian Plagemann, Cyrill Stachniss, Jürgen...
ICIP
2006
IEEE
14 years 10 months ago
Denoising Archival Films using a Learned Bayesian Model
We develop a Bayesian model of digitized archival films and use this for denoising, or more specifically de-graining, individual frames. In contrast to previous approaches our mod...
Teodor Mihai Moldovan, Stefan Roth, Michael J. Bla...
IJON
2007
88views more  IJON 2007»
13 years 8 months ago
Information maximization in face processing
This perspective paper explores principles of unsupervised learning and how they relate to face recognition. Dependency coding and information maximization appear to be central pr...
Marian Stewart Bartlett