14602

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Vlad Olaru, Mihai Florea, Cristian Sminchisescu
Institute of Mathematics of the Romanian Academy
arXiv:1509.06004 [cs.CV], (20 Sep 2015)

@article{olaru2015parallel,

   title={A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation},

   author={Olaru, Vlad and Florea, Mihai and Sminchisescu, Cristian},

   year={2015},

   month={sep},

   archivePrefix={"arXiv"},

   primaryClass={cs.CV}

}

This paper presents a framework that supports the implementation of parallel solutions for the widespread parametric maximum flow computational routines used in image segmentation algorithms. The framework is based on supergraphs, a special construction combining several image graphs into a larger one, and works on various architectures (multi-core or GPU), either locally or remotely in a cluster of computing nodes. The framework can also be used for performance evaluation of parallel implementations of maximum flow algorithms. We present the case study of a state-of-the-art image segmentation algorithm based on graph cuts, Constrained Parametric Min-Cut (CPMC), that uses the parallel framework to solve parametric maximum flow problems, based on a GPU implementation of the well-known push-relabel algorithm. Our results indicate that real-time implementations based on the proposed techniques are possible.
Rating: 2.5. From 2 votes.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: