Volumetric Nonlinear Anisotropic Diffusion on GPUs
Autor: | Chandrajit L. Bajaj, Thomas Kalbe, Michael Goesele, Arjan Kuijper, Andreas Schwarzkopf |
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Rok vydání: | 2012 |
Předmět: |
Hessian matrix
Anisotropic diffusion ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION MathematicsofComputing_NUMERICALANALYSIS Geometry Projection (linear algebra) Computational science Nonlinear system symbols.namesake Computer Science::Graphics Tangent space symbols Normal Eigenvalues and eigenvectors Eigendecomposition of a matrix ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
Zdroj: | Lecture Notes in Computer Science ISBN: 9783642247842 SSVM |
DOI: | 10.1007/978-3-642-24785-9_6 |
Popis: | We present an efficient implementation of volumetric nonlinear anisotropic image diffusion on modern programmable graphics processing units (GPUs). We avoid the computational bottleneck of a time consuming eigenvalue decomposition in ℝ3. Instead, we use a projection of the Hessian matrix along the surface normal onto the tangent plane of the local isodensity surface and solve for the remaining two tangent space eigenvectors. We derive closed formulas to achieve this resulting in efficient GPU code. We show that our most complex volumetric nonlinear anisotropic diffusion gains a speed up of more than 600 compared to a CPU solution. |
Databáze: | OpenAIRE |
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