Minimum Square Distance Thresholding Based on Asymmetrical Co-occurrence Matrix

  • Hong Zhang
  • Qiang ZhiEmail author
  • Fan Yang
  • Jiulun Fan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


In thresholded image segmentation, correct and adequate extraction of pixel distribution information is the key. In this paper, asymmetrical gray transition co-occurrence matrix is applied to better represent the spatial distribution information of images, and uniformity probability of binarization image is introduced to calculated the deviation information between original and thresholding image. A novel minimum square distance criterion function is proposed to select threshold value, and the vector correlation coefficient is deduced to interpret the reasonable of new criterion. Comparing with relative entropy method, the proposed method is simpler, moreover, it has outstanding object extraction performance.


Thresholding method Co-occurrence matrix Minimum square distance 



This work is supported by the National Science Foundation of China (No. 61571361, 61671377), and the Science Plan Foundation of the Education Bureau of Shaanxi Province (No. 15JK1682).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hong Zhang
    • 1
    • 2
  • Qiang Zhi
    • 1
    • 2
    Email author
  • Fan Yang
    • 1
    • 2
  • Jiulun Fan
    • 3
  1. 1.Xi’an UniversityXi’anChina
  2. 2.School of AutomationXi’an University of Posts and TelecommunicationsXi’anChina
  3. 3.Xi’an University of Posts and TelecommunicationsXi’anChina

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