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CORR
2011
Springer
194views Education» more  CORR 2011»
8 years 6 months ago
Sparse approximation property and stable recovery of sparse signals from noisy measurements
—In this paper, we introduce a sparse approximation property of order s for a measurement matrix A: xs 2 ≤ D Ax 2 + β σs(x) √ s for all x, where xs is the best s-sparse app...
Qiyu Sun
SIAMMAX
2010
116views more  SIAMMAX 2010»
8 years 9 months ago
Structured Total Maximum Likelihood: An Alternative to Structured Total Least Squares
Abstract. Linear inverse problems with uncertain measurement matrices appear in many different applications. One of the standard techniques for solving such problems is the total l...
Amir Beck, Yonina C. Eldar
CORR
2010
Springer
94views Education» more  CORR 2010»
8 years 12 months ago
Segmented compressed sampling for analog-to-information conversion: Method and performance analysis
A new segmented compressed sampling (CS) method for analog-to-information conversion (AIC) is proposed. An analog signal measured by a number of parallel branches of mixers and int...
Omid Taheri, Sergiy A. Vorobyov
NIPS
2003
9 years 3 months ago
Factorization with Uncertainty and Missing Data: Exploiting Temporal Coherence
The problem of “Structure From Motion” is a central problem in vision: given the 2D locations of certain points we wish to recover the camera motion and the 3D coordinates of ...
Amit Gruber, Yair Weiss
WACV
2005
IEEE
9 years 8 months ago
A Factorization Method for Structure from Planar Motion
We propose a factorization method for structure from planar motion using a stationary perspective camera. Compared with [8] for general motion, our work has three major difference...
Jian Li, Rama Chellappa
SCIA
2009
Springer
305views Image Analysis» more  SCIA 2009»
9 years 9 months ago
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
Carl Olsson, Magnus Oskarsson
ICCV
2007
IEEE
10 years 4 months ago
pLSA for Sparse Arrays With Tsallis Pseudo-Additive Divergence: Noise Robustness and Algorithm
We introduce the Tsallis divergence error measure in the context of pLSA matrix and tensor decompositions showing much improved performance in the presence of noise. The focus of ...
Tamir Hazan, Roee Hardoon, Amnon Shashua
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