A High Performance Parallel Graph Cut Optimization for Depth Estimation

Bo Yen Chen*, Bo-Cheng Lai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Graph-cut has been proved to return good quality on the optimization of depth estimation. Leveraging the parallel computation has been proposed as a solution to handle the intensive computation of graph-cut algorithm. This pa-per proposes two parallelization techniques to enhance the execution time of graph-cut optimization. By executing on an Intel 8-core CPU, the proposed scheme can achieve an average of 4.7 times speedup with only 0.01% energy increase.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Applications - Volume 2
Subtitle of host publicationProceedings of the International Computer
EditorsChang Ruay-Shiung, Peng Sheng-Lung, Lin Chia-Chen
Pages311-320
Number of pages10
DOIs
StatePublished - 28 Jun 2013

Publication series

NameSmart Innovation, Systems and Technologies
Volume21
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Keywords

  • Depth estimation
  • Graph cut
  • Parallelization
  • Stereo correspondence

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  • Cite this

    Chen, B. Y., & Lai, B-C. (2013). A High Performance Parallel Graph Cut Optimization for Depth Estimation. In C. Ruay-Shiung, P. Sheng-Lung, & L. Chia-Chen (Eds.), Advances in Intelligent Systems and Applications - Volume 2: Proceedings of the International Computer (pp. 311-320). (Smart Innovation, Systems and Technologies; Vol. 21). https://doi.org/10.1007/ 978-3-642-35473-1_31