
Exploiting Eigenvalues of the Hessian Matrix for Volume Decimation
Jiří Hladůvka, Meister Eduard GröllerExploiting Eigenvalues of the Hessian Matrix for Volume Decimation
TR-186-2-00-19, October 2000 [
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- Publication Type: Technical Report
- Keywords: Laplacian filter, Eigenvalues, Hessian matrix, Sparse data, Volume Rendering
Abstract
In recent years the Hessian matrix and its eigenvalues became important in pattern recognition. Several algorithms based on the information they provide have been introduced. We recall the relationship between the eigenvalues of Hessian matrix and the 2nd order edge detection filter, show the usefulness of treating them separately and exploit these facts to design a combined threshold operation to generate sparse data sets.Additional Files and Images
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@techreport{Hladuvka-2000-ExpX,
title = "Exploiting Eigenvalues of the Hessian Matrix for Volume
Decimation",
author = "Ji{\v r}{\' i} Hlad{\r u}vka and Meister Eduard
Gr{\"o}ller",
year = "2000",
abstract = "In recent years the Hessian matrix and its eigenvalues
became important in pattern recognition. Several algorithms
based on the information they provide have been introduced.
We recall the relationship between the eigenvalues of
Hessian matrix and the 2nd order edge detection filter, show
the usefulness of treating them separately and exploit
these facts to design a combined threshold
operation to generate sparse data sets.",
address = "Favoritenstrasse 9-11/186, A-1040 Vienna, Austria",
institution = "Institute of Computer Graphics and Algorithms, Vienna
University of Technology",
note = "human contact: technical-report@cg.tuwien.ac.at",
month = oct,
number = "TR-186-2-00-19",
keywords = "Laplacian filter, Eigenvalues, Hessian matrix, Sparse data,
Volume Rendering",
URL = "http://www.cg.tuwien.ac.at/research/publications/2000/Hladuvka-2000-ExpX/",
}