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Laser & Optoelectronics Progress, Volume. 57, Issue 22, 221014(2020)

Image Segmentation Method Based on Dual Feature Markov Random Field

Mingyi Duan1, Yinju Lu1,2、*, and Yu Su1
Author Affiliations
  • 1College of Information Engineering, Zhengzhou University of Technology, Zhengzhou, Henan 450044, China
  • 2School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (18)
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    Figures & Tables(8)
    G-L fractional differential operator template

    Fig. 1. G-L fractional differential operator template

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    Original images. (a) Cameraman; (b) synthetic image

    Fig. 2. Original images. (a) Cameraman; (b) synthetic image

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    Segmentation results under different windows. (a) 3×3; (b) 5×5; (c) 7×7; (d) 9×9

    Fig. 3. Segmentation results under different windows. (a) 3×3; (b) 5×5; (c) 7×7; (d) 9×9

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    Image segmentation results under different temperature coefficients. (a) β=1; (b) β=2; (c) β=4; (d) β=8

    Fig. 4. Image segmentation results under different temperature coefficients. (a) β=1; (b) β=2; (c) β=4; (d) β=8

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    Variation curve of image sequence energy function U(x)

    Fig. 5. Variation curve of image sequence energy function U(x)

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    Segmentation effect of different algorithms. (a1)(b1) MRF; (a2)(b2) FCN; (a3)(b3) GMM; (a4)(b4) D_MRF

    Fig. 6. Segmentation effect of different algorithms. (a1)(b1) MRF; (a2)(b2) FCN; (a3)(b3) GMM; (a4)(b4) D_MRF

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    • Table 1. Commonly used statistical characteristics in co-occurrence matrix

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      Table 1. Commonly used statistical characteristics in co-occurrence matrix

      StatisticsSymbolCalculation formula
      Entropyt1EEN=-∑i=1L∑j=1LP(i,j)lnP(i,j)
      Energyt2E=∑i=1L∑j=1LP(i,j)2
      Correlationt3CCov=Σi=1LΣi=1LijP(i,j)-μiμjσiσj
      Contrastt4C=∑i,jL(i-j)2E
      IDMt5EEE=-∑i=1L∑j=1LP(i,j)1+(i,j)2

    • Table 2. Comparison of results obtained by different algorithms

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      Table 2. Comparison of results obtained by different algorithms

      SampleSegmentation algorithmRJRCCR
      WMGMCSF
      CameramanMRF0.84360.82710.83410.8231
      FCN0.90630.89720.88450.8601
      GMM0.82720.81930.80620.8023
      D_MRF0.93730.92850.92630.9389
      Synthetic imageMRF0.83860.84760.84620.8406
      FCN0.88720.91650.89640.8903
      GMM0.81160.82730.80120.8208
      D_MRF0.94660.93730.93420.9391
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    Mingyi Duan, Yinju Lu, Yu Su. Image Segmentation Method Based on Dual Feature Markov Random Field[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221014

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    Paper Information

    Category: Image Processing

    Received: Mar. 2, 2020

    Accepted: Apr. 20, 2020

    Published Online: Nov. 5, 2020

    The Author Email: Yinju Lu (luyinju2003@163.com)

    DOI:10.3788/LOP57.221014

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology