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 proceedingChapterpeer-review

    1 Scopus citations


    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
    Number of pages10
    StatePublished - 28 Jun 2013

    Publication series

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


    • Depth estimation
    • Graph cut
    • Parallelization
    • Stereo correspondence


    Dive into the research topics of 'A High Performance Parallel Graph Cut Optimization for Depth Estimation'. Together they form a unique fingerprint.

    Cite this