A parallel generalized relaxation method for high-performance image segmentation on GPUs

DOI10.1016/j.cam.2015.04.035zbMath1322.65071OpenAlexW2065973083MaRDI QIDQ747912

Salvatore Filippone, Pasqua D'Ambra

Publication date: 19 October 2015

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2015.04.035

zbMATH Keywords

image segmentationGPUrelaxation methodsvariational models

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Parallel numerical computation (65Y05) Machine vision and scene understanding (68T45) Numerical solution of discretized equations for boundary value problems involving PDEs (65N22) Software, source code, etc. for problems pertaining to numerical analysis (65-04)

Related Items (2)

Efficient Algebraic Multigrid Preconditioners on Clusters of GPUs ⋮ Serial and parallel approaches for image segmentation by numerical minimization of a second-order functional

Uses Software

• 2LEV-D2P4
• ITPACK
• ITPACK 2C
• CUDA
• PSBLAS
• MLD2P4
• CUSPARSE
• Paralution
• STREAM benchmark

Cites Work

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• 2LEV-D2P4: a package of high-performance preconditioners for scientific and engineering applications
• MLD2P4
• Object-Oriented Techniques for Sparse Matrix Computations in Fortran 2003
• Sparse Matrix-Vector Multiplication on GPGPUs
• Approximation of functional depending on jumps by elliptic functional via t-convergence
• PSBLAS
• Mumford and Shah Model and its Applications to Image Segmentation andImage Restoration

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