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ICASSP
2011
IEEE
12 years 10 months ago
Bayesian Compressive Sensing for clustered sparse signals
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. ...
Lei Yu, Hong Sun, Jean-Pierre Barbot, Gang Zheng
CVPR
2010
IEEE
13 years 4 months ago
Growing semantically meaningful models for visual SLAM
Though modern Visual Simultaneous Localisation and Mapping (vSLAM) systems are capable of localising robustly and efficiently even in the case of a monocular camera, the maps prod...
Alexander Flint, Christopher Mei, Ian D. Reid, Dav...
ICASSP
2011
IEEE
12 years 10 months ago
Compressive sensing meets game theory
We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in underdetermined linear regression problems. Given f = Φα∗ + µ, the ...
Sina Jafarpour, Robert E. Schapire, Volkan Cevher
ICRA
2008
IEEE
127views Robotics» more  ICRA 2008»
14 years 1 months ago
New framework for Simultaneous Localization and Mapping: Multi map SLAM
—The main contribution of this paper arises from the development of a new framework, which has its inspiration in the mechanics of human navigation, for solving the problem of Si...
Damith Chandana Herath, Sarath Kodagoda, Gamini Di...
JMLR
2012
11 years 9 months ago
Universal Measurement Bounds for Structured Sparse Signal Recovery
Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
Nikhil S. Rao, Ben Recht, Robert D. Nowak