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» A Geometric Functional for Derivatives Approximation
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ORL
2011
13 years 2 months ago
Convex approximations to sparse PCA via Lagrangian duality
We derive a convex relaxation for cardinality constrained Principal Component Analysis (PCA) by using a simple representation of the L1 unit ball and standard Lagrangian duality. ...
Ronny Luss, Marc Teboulle
IJCV
2010
194views more  IJCV 2010»
13 years 7 months ago
Improving Bag-of-Features for Large Scale Image Search
This article improves recent methods for large scale image search. We first analyze the bag-of-features approach in the framework of approximate nearest neighbor search. This lea...
Herve Jegou, Matthijs Douze, Cordelia Schmid
ICIP
1998
IEEE
14 years 10 months ago
Minimum Support Interpolators with Optimum Approximation Properties
Abstract- We investigate the functions of given approximation order L that have the smallest support. Those are shown to be linear combinations of the Bspline of degree .L - 1 and ...
Michael Unser, Philippe Thévenaz, Thierry B...
CVPR
2004
IEEE
14 years 10 months ago
Globally Optimal Segmentation of Interacting Surfaces with Geometric Constraints
Abstract-- Efficient detection of globally optimal surfaces representing object boundaries in volumetric datasets is important and remains challenging in many medical image analysi...
Kang Li, Xiaodong Wu, Danny Z. Chen, Milan Sonka
ICPR
2008
IEEE
14 years 9 months ago
Solving quadratically constrained geometrical problems using lagrangian duality
In this paper we consider the problem of solving different pose and registration problems under rotational constraints. Traditionally, methods such as the iterative closest point ...
Carl Olsson, Anders Eriksson