倒数粗糙熵图像阈值化分割算法

范九伦; 雷博

doi:10.11999/JEIT190559

倒数粗糙熵图像阈值化分割算法

doi: 10.11999/JEIT190559

范九伦,
雷博^,

1.
西安邮电大学通信与信息工程学院西安 710121
2.
电子信息现场勘验应用技术公安部重点实验室西安 710121

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)

详细信息

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

中图分类号: TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2019-07-25
- 修回日期: 2019-10-25
- 网络出版日期: 2019-11-13
- 刊出日期: 2020-01-21

Image Thresholding Segmentation Method Based on Reciprocal Rough Entropy

Jiulun FAN,
Bo LEI^,

1.
School of Communication and Information Engineering, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
2.
Key Laboratory of Electronic Information Application Technology for Scene Investigation, Public Security Ministry, Xi’an 710121, China

Funds: The National Natural Science Foundation of China(61671377, 61571361, 61601362), The Project of New Star Team of Xi’an University of Posts & Telecommunications (xyt2016-01)

摘要

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。
- 图像处理 /
- 阈值分割 /
- 粗糙熵 /
- 倒数粗糙熵 /
- 粒化
Abstract:
Image thresholding methods based on the rough entropy segment the images without prior information except the images. There are two problems to be considered in the rough entropy based thresholding methods, i.e., measuring the incompleteness of knowledge about an image and granulating the image. In this paper, reciprocal rough entropy, a new form of rough entropy, is defined to measure the incompleteness of the image information. In order to granulate the image effectively, a granule size selection method based on the homogeneity histogram is employed. The proposed reciprocal rough entropy is simple in expression and calculation. The experimental results verify the effectiveness of the proposed algorithm.
- Image processing /
- Thresholding segmentation /
- Rough entropy /
- Reciprocal rough entropy /
- Granulation

HTML全文

图 1 cameraman 图像的直方图和均匀性直方图

下载: 全尺寸图片幻灯片

图 2 均匀性直方图及最小峰宽度

下载: 全尺寸图片幻灯片

图 3 NDT image1分割结果

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图 4 NDT image2分割结果

下载: 全尺寸图片幻灯片

图 5 OTCBVS\库5\irw02\000215分割结果

下载: 全尺寸图片幻灯片

图 6 OTCBVS\库5\irw06\000225分割结果

下载: 全尺寸图片幻灯片

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 image1	177	51	(151,151)	83	116	221
NDT image2	52	177	(106,115)	45	160	72
D5\irw02\000215	68	75	(66,70)	46	148	211
D5\irw06\000225	65	75	(66,67)	46	128	209

下载: 导出CSV

表 2 6种算法的ME值与SSIM值比较

	NDT image1		NDT image2		D5\irw02\000215		D5\irw06\000225
	ME	SSIM	ME	SSIM	ME	SSIM	ME	SSIM
最大粗糙熵法	0.3605	0.0283	0.1996	0.5880	0.5556	0.0011	0.5671	0.0021
模糊熵法	0.9507	0.0015	0.2250	0.2345	0.5082	0.0013	0.4722	0.0029
罗的方法	0.6341	0.0098	0.0077	0.9822	0.5596	0.0013	0.5679	0.0021
Masi熵法	0.9136	0.0033	0.5470	0.1658	0.6841	0.0007	0.7181	0.0013
倒数熵法	0.8486	0.0049	0.2041	0.3172	0.0366	0.1367	0.0461	0.1585
倒数粗糙熵法	0.0016	0.9765	0.0429	0.9015	0.0051	0.7286	0.0084	0.6833

下载: 导出CSV

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