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倒数粗糙熵图像阈值化分割算法

范九伦 雷博

引用本文: 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 shu
Citation:  Jiulun FAN, Bo LEI. Image Thresholding Segmentation Method Based on Reciprocal Rough Entropy[J]. Journal of Electronics and Information Technology, 2020, 42(1): 214-221. doi: 10.11999/JEIT190559 shu

倒数粗糙熵图像阈值化分割算法

    作者简介: 范九伦: 男,1964年生,教授,研究方向为模糊集理论、模糊信息处理、模式识别与图像处理、信息安全;
    雷博: 女,1981年生,副教授,研究方向为模糊信息处理、粗糙集理论、图像分割
    通讯作者: 雷博,leileibo@xupt.edu.cn
  • 基金项目: 国家自然科学基金(61671377, 61571361, 61601362),西安邮电大学西邮新星团队项目(xyt2016-01)

摘要: 基于粗糙集理论的粗糙熵阈值法不需要图像之外的先验信息。粗糙熵阈值法需要解决两个问题,一是图像信息不完整性的度量,二是图像的粒化。该文基于倒数信息熵,提出一种倒数粗糙熵用来度量图像中信息的不完整性。为了更好地对图像进行粒化,采用一种基于均匀性直方图的粒子选取方式。该文提出的倒数粗糙熵表述简洁,计算简单。实验验证了该文方法的有效性。

English

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  • 图 1  cameraman 图像的直方图和均匀性直方图

    图 2  均匀性直方图及最小峰宽度

    图 3  NDT image1分割结果

    图 6  OTCBVS\库5\irw06\000225分割结果

    图 4  NDT image2分割结果

    图 5  OTCBVS\库5\irw02\000215分割结果

    表 1  6种算法的阈值比较

    最大粗糙熵法模糊熵法罗的方法Masi熵法倒数熵法倒数粗糙熵法
    NDT image117751(151,151)83116221
    NDT image252177(106,115)4516072
    D5\irw02\0002156875(66,70)46148211
    D5\irw06\0002256575(66,67)46128209
    下载: 导出CSV

    表 2  6种算法的ME值与SSIM值比较

    NDT image1NDT image2D5\irw02\000215D5\irw06\000225
    MESSIMMESSIMMESSIMMESSIM
    最大粗糙熵法0.36050.02830.19960.58800.55560.00110.56710.0021
    模糊熵法0.95070.00150.22500.23450.50820.00130.47220.0029
    罗的方法0.63410.00980.00770.98220.55960.00130.56790.0021
    Masi熵法0.91360.00330.54700.16580.68410.00070.71810.0013
    倒数熵法0.84860.00490.20410.31720.03660.13670.04610.1585
    倒数粗糙熵法0.00160.97650.04290.90150.00510.72860.00840.6833
    下载: 导出CSV
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文章相关
  • 通讯作者:  雷博, leileibo@xupt.edu.cn
  • 收稿日期:  2019-07-25
  • 录用日期:  2019-10-25
  • 网络出版日期:  2019-11-13
  • 刊出日期:  2020-01-01
通讯作者: 陈斌, bchen63@163.com
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