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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
  • [1] 吴一全, 孟天亮, 吴诗婳. 图像阈值分割方法研究进展20年(1994–2014)[J]. 数据采集与处理, 2015, 30(1): 1–23. doi: 10.16337/j.1004-9037.2015.01.001

    WU Yiquan, MENG Tianliang, and WU Shihua. Research progress of image thresholding methods in recent 20 years (1994–2014)[J]. Journal of Data Acquisition and Processing, 2015, 30(1): 1–23. doi: 10.16337/j.1004-9037.2015.01.001
    [2] 范九伦, 雷博. 倒数粗糙熵图像阈值化分割算法[J]. 电子与信息学报, 2020, 42(1): 214–221. doi: 10.11999/JEIT190559

    FAN Jiulun and LEI Bo. Image thresholding segmentation method based on reciprocal rough entropy[J]. Journal of Electronics &Information Technology, 2020, 42(1): 214–221. doi: 10.11999/JEIT190559
    [3] SEZGIN M and SANKUR B. Survey over image thresholding techniques and quantitative performance evaluation[J]. Journal of Electronic Imaging, 2004, 13(1): 146–168. doi: 10.1117/1.1631315
    [4] KAPUR J N, SAHOO P K, and WONG A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics, and Image Processing, 1985, 29(3): 273–285. doi: 10.1016/0734-189X(85)90125-2
    [5] 吴一全, 张金矿. 二维直方图θ划分最大Shannon熵图像阈值分割[J]. 物理学报, 2010, 59(8): 5487–5495. doi: 10.7498/aps.59.5487

    WU Yiquan and ZHANG Jinkuang. Image thresholding based on θ-division of 2-D histogram and maximum Shannon entropy[J]. Acta Physica Sinica, 2010, 59(8): 5487–5495. doi: 10.7498/aps.59.5487
    [6] SAHOO P, WILKINS C, and YEAGER J. Threshold selection using Rényi's entropy[J]. Pattern Recognition, 1997, 30(1): 71–84. doi: 10.1016/S0031-3203(96)00065-9
    [7] WEI Wei. Gray image thresholding based on three-dimensional Rényi entropy[C]. The 6th International Congress on Image and Signal Processing, Hangzhou, China, 2013: 599–603.
    [8] 龙建武, 申铉京, 魏巍, 等. 一种结合纹理信息的三维Rényi熵阈值分割算法[J]. 小型微型计算机系统, 2011, 32(5): 948–952.

    LONG Jianwu, SHEN Xuanjing, WEI Wei, et al. 3-D Rényi entropy thresholding algorithm combining with the texture[J]. Journal of Chinese Computer Systems, 2011, 32(5): 948–952.
    [9] DE ALBUQUERQUE M P, ESQUEF I A, MELLO A R G, et al. Image thresholding using Tsallis entropy[J]. Pattern Recognition Letters, 2004, 25(9): 1059–1065. doi: 10.1016/j.patrec.2004.03.003
    [10] 吴一全, 张金矿. 二维直方图θ-划分Tsallis熵阈值分割算法[J]. 信号处理, 2010, 26(8): 1162–1168. doi: 10.3969/j.issn.1003-0530.2010.08.008

    WU Yiquan and ZHANG Jinkuang. Image thresholding based on 2-D histogram θ-division and Tsallis entropy[J]. Signal Processing, 2010, 26(8): 1162–1168. doi: 10.3969/j.issn.1003-0530.2010.08.008
    [11] YE Zhiwei, YANG Juan, WANG Mingwei, et al. 2D Tsallis entropy for image segmentation based on modified chaotic bat algorithm[J]. Entropy, 2018, 20(4): 239. doi: 10.3390/e20040239
    [12] ZHANG Hong. One-dimensional Arimoto entropy threshold segmentation method based on parameters optimization[C]. 2011 International Conference on Applied Informatics and Communication, Xi’an, China, 2011: 573–581.
    [13] 卓问, 曹治国, 肖阳. 基于二维Arimoto熵的阈值分割方法[J]. 模式识别与人工智能, 2009, 22(2): 208–213. doi: 10.3969/j.issn.1003-6059.2009.02.007

    ZHUO Wen, CAO Zhiguo, and XIAO Yang. Image thresholding based on two-dimensional Arimoto entropy[J]. Pattern Recognition and Artificial Intelligence, 2009, 22(2): 208–213. doi: 10.3969/j.issn.1003-6059.2009.02.007
    [14] NIE Fangyan, ZHANG Pingfeng, LI Jianqi, et al. A novel generalized entropy and its application in image thresholding[J]. Signal Processing, 2017, 134: 23–34. doi: 10.1016/j.sigpro.2016.11.004
    [15] SPARAVIGNA A C. Bi-level image thresholding obtained by means of Kaniadakis entropy[EB/OL]. https://arxiv.org/vc/arxiv/papers/1502/1502.04500v2.pdf, 2021.
    [16] LI C H and LEE C K. Minimum cross entropy thresholding[J]. Pattern Recognition, 1993, 26(4): 617–625. doi: 10.1016/0031-3203(93)90115-D
    [17] AL-OSAIMI G and EL-ZAART A. Minimum cross entropy thresholding for SAR images[C]. The 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria, 2008: 1245–1250.
    [18] ZHU Zhenfeng, LU Hanqing, and ZHAO Yao. Scale multiplication in odd Gabor transform domain for edge detection[J]. Journal of Visual Communication and Image Representation, 2007, 18(1): 68–80. doi: 10.1016/j.jvcir.2006.10.001
    [19] LINDEBERG T. Scale-space for discrete signals[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(3): 234–254. doi: 10.1109/34.49051
    [20] SUYARI H and TSUKADA M. Law of error in Tsallis statistics[J]. IEEE Transactions on Information Theory, 2005, 51(2): 753–757. doi: 10.1109/TIT.2004.840862
    [21] 闫海霞, 赵晓晖. 基于Tsallis熵差的遥感图像边缘检测方法[J]. 计算机应用研究, 2009, 26(9): 3598–3600. doi: 10.3969/j.issn.1001-3695.2009.09.117

    YAN Haixia and ZHAO Xiaohui. Edge detection method based on Tsallis entropy difference of remote sensing image[J]. Application Research of Computers, 2009, 26(9): 3598–3600. doi: 10.3969/j.issn.1001-3695.2009.09.117
    [22] TSALLIS C. Possible generalization of Boltzmann-Gibbs statistics[J]. Journal of Statistical Physics, 1988, 52(1/2): 479–487. doi: 10.1007/BF01016429
    [23] BEMIS R. Thresholding tool[EB/OL]. http://cn.mathworks.com/matlabcentral/fileexchange/6770-thresholding-tool, 2022.
    [24] CAI Hongmin, YANG Zhong, CAO Xinhua, et al. A new iterative triclass thresholding technique in image segmentation[J]. IEEE Transactions on Image Processing, 2014, 23(3): 1038–1046. doi: 10.1109/TIP.2014.2298981
    [25] LEI Tao, JIA Xiaohong, ZHANG Yanning, et al. Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(5): 3027–3041. doi: 10.1109/TFUZZ.2018.2796074
    [26] WANG Dong and WANG Xiaoping. The Iterative Convolution-Thresholding Method (ICTM) for image segmentation[EB/OL]. https://arxiv.org/pdf/1904.10917.pdf, 2021.
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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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