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» On sparse signal representations
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ICASSP
2008
IEEE
16 years 1 months ago
Blind separation of non-negative sources by convex analysis: Effective method using linear programming
We recently reported a criterion for blind separation of non-negative sources, using a new concept called convex analysis for mixtures of non-negative sources (CAMNS). Under some ...
Tsung-Han Chan, Wing-Kin Ma, Chong-Yung Chi, Yue W...
ICASSP
2011
IEEE
14 years 10 months ago
The value of redundant measurement in compressed sensing
The aim of compressed sensing is to recover attributes of sparse signals using very few measurements. Given an overall bit budget for quantization, this paper demonstrates that th...
Victoria Kostina, Marco F. Duarte, Sina Jafarpour,...
CVPR
2009
IEEE
16 years 11 months ago
Early Spatiotemporal Grouping with a Distributed Oriented Energy Representation
Spatiotemporal data is associated with vast amounts of raw samples. Given the limited computational resources typically available, an initial organization of this data supporting ...
Konstantinos G. Derpanis, Richard P. Wildes
ICMLA
2004
15 years 8 months ago
Emergent representations and reasoning in adaptive agents
Classically, cognition assumes that the underlying mechanisms of thinking are based on symbol manipulation processes. This assumption has several drawbacks, such as the issue of w...
Joost Broekens, Doug DeGroot
CVPR
2006
IEEE
16 years 8 months ago
Image Denoising with Shrinkage and Redundant Representations
Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal's ...
Michael Elad, Boaz Matalon, Michael Zibulevsky