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TIP
2011
123views more  TIP 2011»
14 years 7 months ago
Spatial Sparsity-Induced Prediction (SIP) for Images and Video: A Simple Way to Reject Structured Interference
We propose a prediction technique that is geared toward forming successful estimates of a signal based on a correlated anchor signal that is contaminated with complex interference...
Gang Hua, Onur G. Guleryuz
117
Voted
JDCTA
2010
119views more  JDCTA 2010»
14 years 7 months ago
Distributed Dual Cluster Algorithm Based on Grid for Sensor Streams
In practical applications, Wireless Sensor Networks generate massive data streams with the dual attributes in geography and optimization domain. Energy source of sensor nodes in W...
Jianghua Huang, Junying Zhang
140
Voted
SIAMIS
2010
156views more  SIAMIS 2010»
14 years 7 months ago
Learning the Morphological Diversity
This article proposes a new method for image separation into a linear combination of morphological components. Sparsity in fixed dictionaries is used to extract the cartoon and osc...
Gabriel Peyré, Jalal Fadili, Jean-Luc Starc...
TNN
2010
234views Management» more  TNN 2010»
14 years 7 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
127
Voted
ICASSP
2011
IEEE
14 years 4 months ago
Collaborative sources identification in mixed signals via hierarchical sparse modeling
A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on CHiLasso, a convex collaborative hierarchical s...
Pablo Sprechmann, Ignacio Ramírez, Pablo Ca...
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