Results 221 to 230 of about 15,595 (260)

Opening the black box: a modular approach to spike sorting

Garcia S, Halcrow C, Windolf C, McKenzie ZM, Adkisson-Floro P, Mayorquin HR, Dichter B, Buccino AP, Yger P.   +8 more
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Matching Pursuit of Images

Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis, 1998
A crucial problem in image analysis is to construct efficient low-level representations of an image, providing precise characterization of features which compose it, such as edges and texture components. An image usually contains very different types of features, which have been successfully modelled by the very redundant family of 2D Gabor oriented ...
F. Bergeaud, Stéphane Mallat
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Multiplivative matching pursuit

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002
This paper introduces a novel nonlinear low-level representation of an image with signal-dependent noise. For multiplicative noisy image, we introduce an algorithm called multiplicative matching pursuit decomposition (MMPD), that decomposes the signal containing the intrinsic variation into a nonlinear expansion of waveforms that are selected from a ...
Amina Serir, Jean-Christophe Pesquet
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On the statistics of matching pursuit angles

Signal Processing, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lisandro Lovisolo, Eduardo A. B. da Silva, Paulo S. R. Diniz   +2 more
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Cyclic adaptive matching pursuit

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
We present an improved Adaptive Matching Pursuit algorithm for computing approximate sparse solutions for overdetermined systems of equations. The algorithms use a greedy approach, based on a neighbor permutation, to select the ordered support positions followed by a cyclical optimization of the selected coefficients. The sparsity level of the solution
Alexandru Onose, Bogdan Dumitrescu
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Knowledge-enhanced Matching Pursuit

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
Compressive Sensing is possible when the sensing matrix acts as a near isometry on signals of interest that can be sparsely or compressively represented. The attraction of greedy algorithms such as Orthogonal Matching Pursuit is their simplicity. However they fail to take advantage of both the structure of the sensing matrix and any prior information ...
Yuejie Chi, A. Robert Calderbank
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A genetic matching pursuit algorithm

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003
Advanced signal processing (SP) methods often lead to greedy algorithms that are time consuming, so useless in real time applications. However, nowadays, efficient implementations of such algorithms become more and more feasible, eventually with the help of concepts withdrawn from fields outside SP.
Dan Stefanoiu, Florin Ionescu
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Matching pursuit filter design

Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), 2002
A method has been devised of using localized information to detect objects with varying signatures without prior segmentation. The detection is performed by a new class of nonlinear filters called matching pursuit filters, which are trained on multiple examples of the object of interest. Matching pursuit filters are designed through a generalization of
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Matching pursuit with damped sinusoids

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002
The matching pursuit algorithm derives an expansion of a signal in terms of the elements of a large dictionary of time-frequency atoms. This paper considers the use of matching pursuit for computing signal expansions in terms of damped sinusoids. First, expansion based on complex damped sinusoids is explored; it is shown that the expansion can be ...
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