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» Covariant Derivatives and Vision
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ICPR
2008
IEEE
14 years 9 months ago
Probabilistic tracking on Riemannian manifolds
The covariance region descriptor recently proposed in [1] has been proved robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of d...
Bo Wu, Hanqing Lu, Jia Liu, Yi Wu
TSP
2008
103views more  TSP 2008»
13 years 8 months ago
Bounds for Estimation of Covariance Matrices From Heterogeneous Samples
Abstract--This correspondence derives lower bounds on the meansquare error (MSE) for the estimation of a covariance matrix , using samples k = 1; . . . ; K, whose covariance matric...
Olivier Besson, Stéphanie Bidon, Jean-Yves ...
ECCV
2010
Springer
13 years 8 months ago
Tensor Sparse Coding for Region Covariances
Sparse representation of signals has been the focus of much research in the recent years. A vast majority of existing algorithms deal with vectors, and higher
Ravishankar Sivalingam, Daniel Boley, Vassilios Mo...
TSP
2008
113views more  TSP 2008»
13 years 8 months ago
Covariance Matrix Estimation With Heterogeneous Samples
We consider the problem of estimating the covariance matrix of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly ...
Olivier Besson, Stéphanie Bidon, Jean-Yves ...
CVPR
2007
IEEE
14 years 10 months ago
Human Detection via Classification on Riemannian Manifolds
We present a new algorithm to detect humans in still images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, well known m...
Oncel Tuzel, Fatih Porikli, Peter Meer