DocumentCode
2116088
Title
Efficient anisotropic α-Kernels decompositions and flows
Author
Feigin, Micha ; Sochen, Nir ; Vemuri, Baba C.
Author_Institution
Sch. of Math., Tel Aviv Univ., Tel Aviv
fYear
2008
fDate
23-28 June 2008
Firstpage
1
Lastpage
8
Abstract
The Laplacian raised to fractional powers can be used to generate scale spaces as was shown in recent literature. This was later extended for inhomogeneous diffusion processes and more general functions of the Laplacian and studied for the Perona-Malik case. In this paper we extend the results to the truly anisotropic Beltrami flow. We additionally introduce a technique for splitting up the work into smaller patches of the image which greatly reduce the computational complexity and allow for the parallelization of the algorithm. Important issues involved in the numerical implementation are discussed.
Keywords
computational complexity; graph theory; image segmentation; image sequences; parallel algorithms; Perona-Malik case; algorithm parallelization; anisotropic Beltrami flow; anisotropic alpha-kernel decomposition; anisotropic alpha-kernel flow; computational complexity; graph Laplacian method; image patch; inhomogeneous diffusion process; scale space analysis; Anisotropic magnetoresistance; Eigenvalues and eigenfunctions; Graph theory; Information science; Laplace equations; Mathematics; Power engineering computing; Power generation; Space heating; Symmetric matrices;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
Conference_Location
Anchorage, AK
ISSN
2160-7508
Print_ISBN
978-1-4244-2339-2
Electronic_ISBN
2160-7508
Type
conf
DOI
10.1109/CVPRW.2008.4562982
Filename
4562982
Link To Document