Title :
Parallel quadratic programming for image processing
Author :
Brand, Matthew ; Chen, Donghui
Author_Institution :
Mitsubishi Electr. Res. Labs., Cambridge, MA, USA
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;
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2011.6116089
