Fast 2D cumulative residual Tsallis entropy threshold segmentation method

HUANG Cong; ZOU Yao-bin

doi:10.37188/CJLCD.2022-0427

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Fast 2D cumulative residual Tsallis entropy threshold segmentation method

Image Segmentation | 更新时间：2023-11-03

- Fast 2D cumulative residual Tsallis entropy threshold segmentation method
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 11, Pages: 1600-1614(2023)
- 作者机构：
  
  1.三峡大学水电工程智能视觉监测湖北省重点实验室，湖北宜昌 443002
  2.三峡大学计算机与信息学院，湖北宜昌 443002
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61871258)
- DOI：10.37188/CJLCD.2022-0427
  CLC：
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HUANG Cong, ZOU Yao-bin. Fast 2D cumulative residual Tsallis entropy threshold segmentation method. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1600-1614(2023)
DOI：

HUANG Cong, ZOU Yao-bin. Fast 2D cumulative residual Tsallis entropy threshold segmentation method. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1600-1614(2023) DOI： 10.37188/CJLCD.2022-0427.

摘要

对灰度直方图呈现为双峰的图像，传统的二维直方图阈值分割方法虽然比较有效，但在灰度直方图呈现为无峰、单峰或多峰模式时，它们的分割结果较差。考虑到经过二维直方图映射得到的二维生存函数存在密度连续和形态统一等优点，本文基于图像二维生存函数提出一种快速二维累积剩余Tsallis熵阈值分割方法。该方法首先基于二维直方图构造二维生存函数，然后在二维生存函数的基础上定义计算分割阈值的二维累积剩余Tsallis熵目标函数。通过递推算法将计算目标函数的时间复杂度降为, $O (L^{2})$ ,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49590067&type=,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49590056&type=,8.80533314,3.21733332,。最后，基于递推形式的二维累积剩余Tsallis熵准则得到最优阈值向量以进行阈值分割。在26幅合成图像和76幅真实世界图像上将提出的方法与2种快速二维阈值分割方法、2种聚类分割方法以及1种活动轮廓分割方法分别在时间和误分类率（Misclassification Error，ME）2个指标下进行了比较。实验结果表明，在合成图像和真实世界图像中，相比于性能第2的方法，本文方法的时间平均缩短0.013 s，ME值平均降低0.051~0.089。提出的快速二维累积剩余Tsallis熵阈值分割方法不仅在计算效率方面优于对比的5种方法，而且在分割适应性和分割精度方面具有明显优势。

Abstract

For images with bimodal gray-level histogram， the traditional two-dimensional histogram threshold segmentation method is more effective， but when gray-level histogram is non-peak， unimodal or multimodal， their segmentation results are poor. Considering that the two-dimensional survival function obtained by two-dimensional histogram mapping has the advantages of continuous density and uniform morphology， a fast two-dimensional cumulative residual Tsallis entropy threshold segmentation method is proposed based on the two-dimensional survival function of images. The method firstly constructs a two-dimensional survival function based on the two-dimensional histogram， and then a two-dimensional cumulative residual Tsallis entropy objective function is defined to compute the segmentation threshold on the basis of the two-dimensional survival function. Further， a recursive algorithm is used to reduce time complexity of calculating the objective function to , $O (L^{2})$ ,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49590421&type=,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49590411&type=,10.15999985,3.80999994,. Finally， based on the two-dimensional cumulative residual Tsallis entropy criterion in recursive form， an optimal threshold vector is obtained for threshold segmentation. In 26 synthetic images and 76 real-world images， the proposed method is compared with two fast two-dimensional threshold segmentation methods， two clustering segmentation methods and one active contour segmentation method respectively under two indicators of time and misclassification error （ME）. Experimental results show that the time is shortened by 0.013 s， and ME value is reduced by 0.051~0.089 on average in comparison with the method of performance 2 in both synthetic and real-world images. The proposed fast two-dimensional cumulative residual Tsallis entropy threshold segmentation method is not only superior to the 5 comparison methods in computational efficiency， but also has relatively obvious advantages in segmentation adaptability and segmentation accuracy.

关键词

阈值分割二维直方图二维生存函数累积剩余Tsallis熵快速递推算法

Keywords

threshold segmentationtwo-dimensional histogramtwo-dimensional survival functioncumulative residual Tsallis entropyfast recursive algorithm

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