A FAST ALGORITHM OF IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD

被引：0
|
作者：
Li, Zhi-Hui [1 ]
Zhang, Meng [1 ]
Liu, Hai-Bo [1 ]
机构：
[1] Harbin Engn Univ, Sch Comp Sci & Technol, Harbin, Peoples R China
来源：
2012 INTERNATIONAL CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (LCWAMTIP) | 2012年
关键词：
Image Segmentation; MAP; MRF;
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：
Markov random field (MRF) is a common used consistent approach in image segmentation. However it has a drawback of low computation speed. A fast definite algorithm of Markov random field is proposed in this paper, which is based on common used frame of maximum posterior probability and Potts model. A representation of binary label field is adopted to describe the membership of each pixel to one of classes. The label fields are derived by iterations of computation. During one time of iteration the label fields are updated on basis of the results of last iteration. The energy function of MRF is computed by mean filter. The running speed of the algorithm is increased while the smoothness effect remains as same as other algorithms. The experiment results in the paper show the effectiveness of the algorithm.
引用
收藏
页码：117 / 120
页数：4
相关论文
共 50 条
  • [31] A region Markov random field model with integrated edge feature and image segmentation algorithm
    MOE Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, China
    不详
    [J]. Hsi An Chiao Tung Ta Hsueh, 2 (14-19):
  • [32] A fuzzy logic model based Markov random field for medical image segmentation
    Nguyen T.M.
    Wu Q.M.J.
    [J]. Evolving Systems, 2013, 4 (3) : 171 - 181
  • [33] A new image segmentation algorithm based on the fusion of Markov Random Field and Fuzzy C-Means Clustering
    Liu, SY
    Li, XF
    Li, ZM
    [J]. International Symposium on Communications and Information Technologies 2005, Vols 1 and 2, Proceedings, 2005, : 139 - 142
  • [34] A novel pixon-representation for image segmentation based on Markov random field
    Lin, Lei
    Zhu, Litao
    Yang, Faguo
    Jiang, Tianzi
    [J]. IMAGE AND VISION COMPUTING, 2008, 26 (11) : 1507 - 1514
  • [35] Efficient Image Segmentation Method Based on Probabilistic Markov Random Field Model
    Sophia, P.
    Venkateswaran, N.
    [J]. 2014 INTERNATIONAL CONFERENCE ON COMMUNICATION AND NETWORK TECHNOLOGIES (ICCNT), 2014, : 95 - 99
  • [36] An integration scheme for image segmentation and labeling based on Markov random field model
    Kim, IY
    Yang, HS
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1996, 18 (01) : 69 - 73
  • [37] Texture Segmentation of Jacquard Fabric Image Based on Multiresolution Markov Random Field
    Tong, Youcheng
    Zhang, Yang
    Yao, Junzhou
    [J]. ADVANCES IN ENGINEERING DESIGN AND OPTIMIZATION II, PTS 1 AND 2, 2012, 102-102 : 496 - +
  • [38] Texture segmentation of multispectral remote sensing image based on Markov Random Field
    Gong, Yan
    Shu, Ning
    [J]. Geomatics and Information Science of Wuhan University, 2007, 32 (03) : 212 - 215
  • [39] Gaussian Markov random field based improved texture descriptor for image segmentation
    Dharmagunawardhana, Chathurika
    Mahmoodi, Sasan
    Bennett, Michael
    Niranjan, Mahesan
    [J]. IMAGE AND VISION COMPUTING, 2014, 32 (11) : 884 - 895
  • [40] An image change detection algorithm based on Markov random field models
    Kasetkasem, T
    Varshney, PK
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2002, 40 (08): : 1815 - 1823
12345