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» On Sparsity and Overcompleteness in Image Models
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ICASSP
2008
IEEE
14 years 3 days ago
Sparse and shift-invariant feature extraction from non-negative data
In this paper we describe a technique that allows the extraction of multiple local shift-invariant features from analysis of non-negative data of arbitrary dimensionality. Our app...
Paris Smaragdis, Bhiksha Raj, Madhusudana V. S. Sh...
CVPR
2008
IEEE
14 years 7 months ago
Enhanced biologically inspired model
It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challengin...
Yongzhen Huang, Kaiqi Huang, Liangsheng Wang, Dach...
CVPR
2012
IEEE
11 years 8 months ago
Rolling shutter bundle adjustment
This paper introduces a bundle adjustment (BA) method that obtains accurate structure and motion from rolling shutter (RS) video sequences: RSBA. When a classical BA algorithm pro...
Johan Hedborg, Per-Erik Forssén, Michael Fe...
CORR
2010
Springer
210views Education» more  CORR 2010»
13 years 5 months ago
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
Tomer Faktor, Yonina C. Eldar, Michael Elad
MICCAI
2010
Springer
13 years 4 months ago
Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis
The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding ...
Vincent Michel, Evelyn Eger, Christine Keribin, Be...