Optics and Precision Engineering, Volume. 31, Issue 12, 1841(2023)

Automatic threshold selection method using exponential Renyi entropy under multi-scale product in stationary wavelet domain

Yaobin ZOU1...2,3,*, Xiangdan MENG2, Shuifa SUN1,3, and Peng CHEN1,23 |Show fewer author(s)
Author Affiliations
  • 1Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering (China Three Gorges University), Yichang 443002, China
  • 2Center for Big Data, China Three Gorges University, Yichang 44300, China
  • 3College of Computer and Information Technology, China Three Gorges University, Yichang 44002, China
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    Figures & Tables(14)
    Multi-scale product effect in the vertical direction ((a) show the original image and its gray level histogram, (b)-(f) show the corresponding MPT image Zv and its gray level histogram when ω is 1, 2, 3, 4 and 5.)
    Schematic diagram of left and right division of gray
    MPT images Zθ and ωθ values corresponding to five wavelets
    Several key steps in the construction of objective function. The gray level histogram Ht is truncated at the frequency 200 for more clearly showing the frequency of gray level in the range 0,255.
    Selected thresholds and the segmentation results of the EREM method on synthetic images with unbalanced target and background sizes
    Selected thresholds and the segmentation results of the EREM method on synthetic images with relatively balanced target and background sizes
    Selected thresholds of the 5 threshold segmentation methods on 4 representative synthetic images
    Segmentation results of 9 methods on 4 synthetic images in Fig. 7(a)-(d)
    Selected thresholds of the 5 threshold segmentation methods on 4 real-world images
    Segmentation results of 9 methods on 4 real-world images in Fig. 9(a)-(d)
    MCC values of 9 segmentation methods on 50 real-world images
    Statistical result of average MCC value for different α
    • Table 1. MCC values of nine segmentation methods on 16 synthetic images in Fig. 5-7

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      Table 1. MCC values of nine segmentation methods on 16 synthetic images in Fig. 5-7

      Test imagesATE25PF26EW27SDD5RSSFCA28FSC29ADRLS30GLFIF31EREM
      Fig.5(a)0.342 30.042 90.728 80.242 20.038 30.027 40.000 00.000 01.000 0
      Fig.5(b)0.007 31.000 00.004 31.000 00.053 50.030 60.916 40.000 01.000 0
      Fig.5(c)0.372 00.084 80.592 60.120 10.045 50.024 00.019 80.000 00.980 6
      Fig.5(d)0.276 10.063 00.644 80.146 70.041 50.017 40.000 00.000 01.000 0
      Fig.5(e)0.339 80.092 40.339 50.082 10.043 30.008 70.000 00.000 00.980 6
      Fig.5(f)0.049 30.039 50.039 50.068 10.037 20.048 80.000 00.000 01.000 0
      Fig.6(a)0.983 20.991 90.985 30.954 00.992 00.946 70.963 80.936 40.992 8
      Fig.6(b)0.983 60.991 00.983 60.980 80.992 00.947 40.963 50.944 90.990 0
      Fig.6(c)0.991 60.992 70.972 00.863 40.991 00.935 80.962 60.946 10.993 5
      Fig.6(d)0.994 40.993 60.979 50.967 60.994 50.934 30.965 90.942 90.994 5
      Fig.6(e)0.308 90.990 80.985 00.774 00.985 00.965 70.963 60.892 00.973 9
      Fig.6(f)0.978 00.922 50.972 40.936 90.856 00.944 50.000 00.901 30.978 0
      Fig.7(a)0.650 40.633 30.643 50.341 80.638 50.895 10.000 00.396 80.994 4
      Fig.7(b)0.734 80.136 30.900 80.473 90.104 00.962 60.940 60.000 01.000 0
      Fig.7(c)0.990 70.988 90.970 00.864 50.981 10.904 60.908 50.883 20.980 1
      Fig.7(d)0.506 60.443 90.488 50.493 50.398 70.873 00.352 50.000 00.994 4
    • Table 2. Comparisons of 9 segmentation methods in computational efficiency

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      Table 2. Comparisons of 9 segmentation methods in computational efficiency

      MethodsCPU time on synthetic imagesCPU time on real-world images
      MeanMean
      EW270.000 0310.000 042
      SDD50.001 3400.002 117
      ATE250.022 2770.024 564
      PF260.020 2650.041 370
      EREM0.061 2230.070 134
      FSC290.329 2340.714 120
      RSSFCA281.811 5773.798 606
      GLFIF312.600 4372.702 787
      ADRLS307.070 47512.368 462
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    Yaobin ZOU, Xiangdan MENG, Shuifa SUN, Peng CHEN. Automatic threshold selection method using exponential Renyi entropy under multi-scale product in stationary wavelet domain[J]. Optics and Precision Engineering, 2023, 31(12): 1841

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    Paper Information

    Category: Information Sciences

    Received: Jul. 18, 2022

    Accepted: --

    Published Online: Jul. 25, 2023

    The Author Email: ZOU Yaobin (zyb@ctgu.edu.cn)

    DOI:10.37188/OPE.20233112.1841

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