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基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法

邹耀斌 张进玉 周欢 孙水发 夏平

邹耀斌, 张进玉, 周欢, 孙水发, 夏平. 基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法[J]. 电子与信息学报, 2023, 45(2): 707-717. doi: 10.11999/JEIT211306
引用本文: 邹耀斌, 张进玉, 周欢, 孙水发, 夏平. 基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法[J]. 电子与信息学报, 2023, 45(2): 707-717. doi: 10.11999/JEIT211306
ZOU Yaobin, ZHANG Jinyu, ZHOU Huan, SUN Shuifa, XIA Ping. Tsallis Entropy Thresholding Based on Multi-scale and Multi-direction Gabor Transform[J]. Journal of Electronics & Information Technology, 2023, 45(2): 707-717. doi: 10.11999/JEIT211306
Citation: ZOU Yaobin, ZHANG Jinyu, ZHOU Huan, SUN Shuifa, XIA Ping. Tsallis Entropy Thresholding Based on Multi-scale and Multi-direction Gabor Transform[J]. Journal of Electronics & Information Technology, 2023, 45(2): 707-717. doi: 10.11999/JEIT211306

基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法

doi: 10.11999/JEIT211306
基金项目: 国家自然科学基金(62172255, 61871258)
详细信息
    作者简介:

    邹耀斌:男,副教授,主要研究方向为数字图像处理、大数据分析、机器学习

    张进玉:女,硕士生,研究方向为数字图像处理

    周欢:男,教授,主要研究方向为移动社交网络、智能信息处理、车联网以及无线传感器网络

    孙水发:男,教授,主要研究方向为智能信息处理、计算机视觉、多媒体信息处理、3维处理及可视化

    夏平:男,教授,主要研究方向为机器学习、智能信息处理、多尺度几何分析及其应用

    通讯作者:

    周欢 zhouhuan117@163.com

  • 中图分类号: TN911.73

Tsallis Entropy Thresholding Based on Multi-scale and Multi-direction Gabor Transform

Funds: The National Natural Science Foundation of China (62172255, 61871258)
  • 摘要: 为了能在统一框架内处理无模态、单模态、双模态或者多模态直方图情形下的自动阈值选取问题,该文提出一种基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法(MGTE)。该方法先通过Gabor变换得到多尺度乘积图像,然后利用内外轮廓图像从多尺度乘积图像中重构1维直方图,并在重构1维直方图上采用Tsallis熵计算模型来选取4个方向Tsallis熵取最大值时对应的阈值,最后对4个方向的阈值进行加权求和作为最终分割阈值。将提出的方法和5个分割方法在4幅合成图像和40幅真实世界图像上进行了实验。结果表明提出的方法虽然计算效率不占优势,但它的分割适应性和分割精度有明显的提高。
  • 图  1  灰度直方图的左右划分示意图

    图  2  分割实验

    图  3  4个模态合成图像的灰度直方图及不同方法所得阈值比较

    图  4  不同分割方法在4个模态合成图像上的分割比较

    图  5  4个不同编号真实世界图像的灰度直方图及不同方法所得阈值比较

    图  6  不同分割方法在4个编号真实世界图像上的分割结果比较

    图  7  6个分割方法在40幅测试图像上的ME值比较

    表  1  6个分割方法在4幅合成图像上的分割阈值$ t $和ME值(%)

    分割方法无模态单模态双模态多模态
    t, MEt, MEt, MEt, ME
    IT201, 0.00214, 0.00129, 0.01209, 0.00
    MGTE201, 0.00212, 0.01129, 0.01214, 0.01
    ITT133, 22.22162, 28.80128, 0.01146, 17.40
    TET126, 24.00151, 48.5773, 21.7098, 28.16
    FRFCM*, 1.17*, 39.43*, 0.17*, 0.87
    ICAC*, 19.58*, 0.02*, 0.00*, 2.95
    下载: 导出CSV

    表  2  5个分割方法的计算效率比较(s)

    分割方法合成图像上CPU耗时真实世界图像上CPU耗时
    均值标准偏差均值标准偏差
    MGTE0.4530.0750.6510.312
    ITT0.0030.0020.0030.002
    TET0.0100.0020.0110.003
    FRFCM0.0650.0640.0300.027
    ICAC0.0270.0190.1940.206
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-22
  • 修回日期:  2022-05-04
  • 录用日期:  2022-05-17
  • 网络出版日期:  2022-05-25
  • 刊出日期:  2023-02-07

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