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» On Sparsity and Overcompleteness in Image Models
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ICASSP
2008
IEEE
15 years 4 months 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
15 years 11 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
13 years 8 hour 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»
14 years 9 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
14 years 8 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...