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ICASSP
2010
IEEE
13 years 7 months ago
Reconstruction of sparse signals from distorted randomized measurements
In this paper we show that, surprisingly, it is possible to recover sparse signals from nonlinearly distorted measurements, even if the nonlinearity is unknown. Assuming just that...
Petros Boufounos
ICCV
2009
IEEE
13 years 5 months ago
Dense 3D reconstruction method using a single pattern for fast moving object
Dense 3D reconstruction of extremely fast moving objects could contribute to various applications such as body structure analysis and accident avoidance and so on. The actual case...
Ryusuke Sagawa, Yuichi Ota, Yasushi Yagi, Ryo Furu...
SIGPRO
2002
160views more  SIGPRO 2002»
13 years 7 months ago
New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction
This paper describes newly invented multiscale transforms known under the name of the ridgelet [6] and the curvelet transforms [9, 8]. These systems combine ideas of multiscale an...
Emmanuel J. Candès, Franck Guo
TSP
2010
13 years 2 months ago
Variance-component based sparse signal reconstruction and model selection
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
Kun Qiu, Aleksandar Dogandzic
JCB
2006
124views more  JCB 2006»
13 years 7 months ago
The Average Common Substring Approach to Phylogenomic Reconstruction
We describe a novel method for efficient reconstruction of phylogenetic trees, based on sequences of whole genomes or proteomes, whose lengths may greatly vary. The core of our me...
Igor Ulitsky, David Burstein, Tamir Tuller, Benny ...