基于图像分割算法的两种碳酸盐岩鲕粒提取方法

Yili Ren, Jian Liang, Siwu Luo
{"title":"基于图像分割算法的两种碳酸盐岩鲕粒提取方法","authors":"Yili Ren, Jian Liang, Siwu Luo","doi":"10.1109/ICISCAE52414.2021.9590702","DOIUrl":null,"url":null,"abstract":"Carbonate rock is a kind of very valuable sedimentary rock, and oolites are one of the most easily identifiable particles in the carbonate rock image. Based on the traditional image segmentation algorithm, this paper proposes two extraction strategies for carbonate oolitic components. The first is the extraction technology of carbonate rock oolites based on traditional image segmentation algorithm: first extract the connected domains of the carbonate rock image, then use the K-Means clustering algorithm to analyze the processed image, and then analyze the image Binary processing, and finally extract the contours of the oolites; the second, the carbonate rock oolite extraction technology based on the superpixel segmentation algorithm: first use the SLIC algorithm to segment the acid rock image; secondly, the road extract. The test results show that the two extraction strategies can clearly extract the oolitic components of salt rock. In addition, according to the experimental results in this paper, it can be seen that the oolitic extraction technology based on SLIC superpixel segmentation is slightly better than that based on traditional image segmentation algorithm.","PeriodicalId":121049,"journal":{"name":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","volume":"89 11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two extraction methods for carbonate rock oolites based on image segmentation algorithm\",\"authors\":\"Yili Ren, Jian Liang, Siwu Luo\",\"doi\":\"10.1109/ICISCAE52414.2021.9590702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Carbonate rock is a kind of very valuable sedimentary rock, and oolites are one of the most easily identifiable particles in the carbonate rock image. Based on the traditional image segmentation algorithm, this paper proposes two extraction strategies for carbonate oolitic components. The first is the extraction technology of carbonate rock oolites based on traditional image segmentation algorithm: first extract the connected domains of the carbonate rock image, then use the K-Means clustering algorithm to analyze the processed image, and then analyze the image Binary processing, and finally extract the contours of the oolites; the second, the carbonate rock oolite extraction technology based on the superpixel segmentation algorithm: first use the SLIC algorithm to segment the acid rock image; secondly, the road extract. The test results show that the two extraction strategies can clearly extract the oolitic components of salt rock. In addition, according to the experimental results in this paper, it can be seen that the oolitic extraction technology based on SLIC superpixel segmentation is slightly better than that based on traditional image segmentation algorithm.\",\"PeriodicalId\":121049,\"journal\":{\"name\":\"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)\",\"volume\":\"89 11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCAE52414.2021.9590702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCAE52414.2021.9590702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

碳酸盐岩是一种非常有价值的沉积岩,而鲕粒是碳酸盐岩图像中最容易识别的颗粒之一。在传统图像分割算法的基础上,提出了两种碳酸盐鲕粒成分的提取策略。首先是基于传统图像分割算法的碳酸盐岩鲕粒提取技术:首先提取碳酸盐岩图像的连通域,然后使用K-Means聚类算法对处理后的图像进行分析,然后对图像进行二值化处理,最后提取鲕粒的轮廓;第二,基于超像素分割算法的碳酸盐岩鲕粒提取技术:首先利用SLIC算法对酸性岩图像进行分割;其次,道路提取。试验结果表明,两种提取策略均能清晰地提取出盐岩中的鲕粒成分。此外,根据本文的实验结果可以看出,基于SLIC超像素分割的鲕粒提取技术略优于基于传统图像分割算法的鲕粒提取技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two extraction methods for carbonate rock oolites based on image segmentation algorithm
Carbonate rock is a kind of very valuable sedimentary rock, and oolites are one of the most easily identifiable particles in the carbonate rock image. Based on the traditional image segmentation algorithm, this paper proposes two extraction strategies for carbonate oolitic components. The first is the extraction technology of carbonate rock oolites based on traditional image segmentation algorithm: first extract the connected domains of the carbonate rock image, then use the K-Means clustering algorithm to analyze the processed image, and then analyze the image Binary processing, and finally extract the contours of the oolites; the second, the carbonate rock oolite extraction technology based on the superpixel segmentation algorithm: first use the SLIC algorithm to segment the acid rock image; secondly, the road extract. The test results show that the two extraction strategies can clearly extract the oolitic components of salt rock. In addition, according to the experimental results in this paper, it can be seen that the oolitic extraction technology based on SLIC superpixel segmentation is slightly better than that based on traditional image segmentation algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信