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» Solving Sparse Linear Constraints
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CSE
2010
IEEE
14 years 10 months ago
MCALab: Reproducible Research in Signal and Image Decomposition and Inpainting
Morphological Component Analysis (MCA) of signals and images is an ambitious and important goal in signal processing; successful methods for MCA have many far-reaching application...
Mohamed-Jalal Fadili, Jean-Luc Starck, Michael Ela...
88
Voted
IJCV
2006
165views more  IJCV 2006»
14 years 10 months ago
A Riemannian Framework for Tensor Computing
Tensors are nowadays a common source of geometric information. In this paper, we propose to endow the tensor space with an affine-invariant Riemannian metric. We demonstrate that ...
Xavier Pennec, Pierre Fillard, Nicholas Ayache
91
Voted
SIAMCOMP
1998
113views more  SIAMCOMP 1998»
14 years 10 months ago
Two-Dimensional Periodicity in Rectangular Arrays
Abstract. String matching is rich with a variety of algorithmic tools. In contrast, multidimensional matching has had a rather sparse set of techniques. This paper presents a new a...
Amihood Amir, Gary Benson
93
Voted
PE
2010
Springer
102views Optimization» more  PE 2010»
14 years 8 months ago
Extracting state-based performance metrics using asynchronous iterative techniques
Solution of large sparse linear fixed-point problems lies at the heart of many important performance analysis calculations. These calculations include steady-state, transient and...
Douglas V. de Jager, Jeremy T. Bradley
TMI
2011
127views more  TMI 2011»
14 years 5 months ago
Reconstruction of Large, Irregularly Sampled Multidimensional Images. A Tensor-Based Approach
Abstract—Many practical applications require the reconstruction of images from irregularly sampled data. The spline formalism offers an attractive framework for solving this prob...
Oleksii Vyacheslav Morozov, Michael Unser, Patrick...