• DocumentCode
    2395818
  • Title

    Integrated segmentation of noisy image based on the spatial relationship

  • Author

    Nguyen, Thanh Minh ; Wu, Q. M Jonathan

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Windsor, Windsor, ON, Canada
  • fYear
    2012
  • fDate
    19-20 May 2012
  • Firstpage
    206
  • Lastpage
    210
  • Abstract
    In this paper, we propose a new algorithm for an integrated image segmentation based on the combination of both Markov Random Fields (MRF) and Graph Cuts (GC). In the well-known GrabCut method, the T-link weights do not take into account the spatial relationship between the neighboring pixels. The proposed algorithm, unlike GrabCut method, incorporates this spatial relationship right into the T-link weights. The performance results obtained using natural images clearly demonstrate the robustness, accuracy and effectiveness of the proposed algorithm, as compared to other known methods.
  • Keywords
    Markov processes; graph theory; image segmentation; GC; GrabCut method; MRF; Markov random fields; T-link weights; graph cuts; integrated image segmentation; natural images; neighboring pixels; noisy image; spatial relationship; Accuracy; Computational modeling; Gaussian noise; Image segmentation; Markov random fields; Object segmentation; Standards; Integrated image segmentation; Markov Random Fields and Graph Cuts;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2012 International Conference on
  • Conference_Location
    Yantai
  • Print_ISBN
    978-1-4673-0198-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2012.6223469
  • Filename
    6223469