DocumentCode
2824411
Title
Parallel quadratic programming for image processing
Author
Brand, Matthew ; Chen, Donghui
Author_Institution
Mitsubishi Electr. Res. Labs., Cambridge, MA, USA
fYear
2011
fDate
11-14 Sept. 2011
Firstpage
2261
Lastpage
2264
Abstract
Many image processing and computer vision problems can be solved as quadratic programs in the nonnegative cone. This paper develops a provably convergent multiplicative update that has a simple form and is amenable to fine-grained data parallelism. Classic algorithms for deblurring, matrix factorization, and tomography are recovered as special cases. This paper also demonstrates applications to super-resolution, labeling and segmentation.
Keywords
computer vision; image resolution; image restoration; image segmentation; matrix decomposition; quadratic programming; classic algorithm; computer vision problems; fine-grained data parallelism; image processing; matrix factorization; parallel quadratic programming; Conferences; Convergence; Image reconstruction; Image resolution; Image segmentation; Labeling; Markov random field; image segmentation; image super-resolution; parallel quadratic programming;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location
Brussels
ISSN
1522-4880
Print_ISBN
978-1-4577-1304-0
Electronic_ISBN
1522-4880
Type
conf
DOI
10.1109/ICIP.2011.6116089
Filename
6116089
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