PARALLEL QUADRATIC PROGRAMMING FOR IMAGE PROCESSING

被引：0
|
作者：
Brand, Matthew [1 ]
Chen, Donghui [2 ]
机构：
[1] Mitsubishi Elect Res Labs, Cambridge, MA USA
[2] Tufts Univ, Medford, MA USA
来源：
2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2011年
关键词：
parallel quadratic programming; image super-resolution; image segmentation; Markov random field; ALGORITHMS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
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页数：4
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