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» Invariant representation in image processing
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139
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ICASSP
2011
IEEE
14 years 7 months ago
An unsupervised algorithm for hybrid/morphological signal decomposition
The main contribution presented here is an adaptive/unsupervised iterative thresholding algorithm for sparse representation of signals which can be modeled as the sum of two compo...
Matthieu Kowalski, Thomas Rodet
115
Voted
ICIP
2004
IEEE
16 years 5 months ago
Estimating the phase congruency of localised frequencies
Phase congruency is a new method for detecting features in images. One of its significant strengths is its invariance to lighting variation within an image, as well as being able ...
Peter Myerscough, Mark S. Nixon
229
Voted
3DIM
2011
IEEE
14 years 3 months ago
Finding the Best Feature Detector-Descriptor Combination
Addressing the image correspondence problem by feature matching is a central part of computer vision and 3D inference from images. Consequently, there is a substantial amount of w...
Anders Lindbjerg Dahl, Henrik Aanæs, Kim Ste...
146
Voted
CORR
2010
Springer
210views Education» more  CORR 2010»
15 years 3 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
151
Voted
ACCV
2010
Springer
14 years 10 months ago
Minimal Representations for Uncertainty and Estimation in Projective Spaces
Abstract. Estimation using homogeneous entities has to cope with obstacles such as singularities of covariance matrices and redundant parametrizations which do not allow an immedia...
Wolfgang Förstner