岩石薄片图像的矿物识别率统计方法
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摘要
岩石薄片图像中矿物颗粒的分割和自动识别对石油地质实验有重要意义。为了评价计算机对岩石薄片图像中矿物颗粒的识别效果,论文提出了一种对岩石薄片图像的矿物识别率进行统计的方法。实验表明,该方法可以良好处理颗粒图像的各种差异问题,实现了计算机的自动统计。
Segmentation and automatic identification of mineral particles in rock slice image are important for petroleum geology experiments. In order to evaluate the computer's recognition effect of the mineral particles in rock slice image,this paper presents a method to calculate the recognition rate of minerals in rock slice image. Experiments show that this method can deal with the various differences of particle images and lead to automatic statistics by the computer.
Segmentation and automatic identification of mineral particles in rock slice image are important for petroleum geology experiments. In order to evaluate the computer's recognition effect of the mineral particles in rock slice image,this paper presents a method to calculate the recognition rate of minerals in rock slice image. Experiments show that this method can deal with the various differences of particle images and lead to automatic statistics by the computer.
引文
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[2]常丽华,陈曼云,金巍,等.透明矿物薄片鉴定手册[M].北京:地质出版社,2006:31-91.CHANG Lihua,CHEN Manyun,JIN Wei,et al.Handbook of Transparent Mineral Slice Identification[M].Beijing:Geological Publishing House,2006:31-91.
[3]Ye Zhou,John Starkey,Lalu Mansinha.Identification of mineral grains in a petrographic thin section using phiand max-images[J].Mathematical Geology,2004,36(7):781-801.
[4]Nock R,Nielsen F.Statistical Region Merging[C]//IEEETransactions on Pattern Analysis and Machine Intelligence,2004,26(11):1452-1458.
[5]吴拥,苏桂芬,滕奇志,等.岩石薄片正交偏光图像的颗粒分割方法[J].科学技术与工程,2013,13(31):9201-9206.WU Yong,SU Guifen,TENG Qizhi,et al.A Particles Segmentation Method of Rock Slice Orthogonal Polarization Images[J].Science Technology and Engineering,2013,13(31):9201-9206.
[6]路达.岩矿薄片偏光序列图像颗粒提取及综合特征识别[D].成都:四川大学电子信息学院,2017.LU Da.Rock Grains Extraction and Feature Recognition of Rocks Polarizing Sequence Image[D].Chengdu:College of Electronics and Information Engineering,Sichuan University,2017.
[7]王倩,王正勇,范艳君,等.基于边缘流和区域合并的岩屑颗粒图像分割[J].四川大学学报(自然科学版),2014,51(1):111-118.WANG Qian,WANG Zhengyong,FAN Yanjun,et al.Image Segmentation of Cutting Grains Based on Edge Flow and Region Merging[J].Journal of Sichuan University(Natural Science Edition),2014,51(1):111-118.
[8]赵启明,王睿,滕奇志,等.基于岩石薄片偏光序列图的颗粒成分分析[J].太赫兹科学与电子信息学报,2015,13(2):285-290.ZHAO Qiming,WANG Rui,TENG Qizhi,et al.Particle Composition Analysis Based on Rock Slice Orthogonal Polarization Sequence Diagram[J].Journal of Terahertz Science and Electronic Information Technology,2015,13(2):285-290.
[9]刘豪,杨永全,郭仙草,等.用于纹理特征提取的改进的LBP算法[J].计算机工程与应用,2014,50(6):182-185.LIU Hao,YANG Yongquan,GUO Xiancao,et al.Improved LBP Used for Texture Feature Extraction[J].Computer Engineering and Applications,2014,50(6):182-185.
[10]谭菊,张友钟.基于灰度共生矩阵的纹理特征景物识别[J].重庆文理学院学报(自然科学版),2010,29(1):66-68.TAN Ju,ZHANG Youzhong.The Scene Identification of Texture Feature Based on Gray Level Co-occurrencematrix[J].Chongqing University of Arts and Sciences(Natural Science Edition),2014,50(6):182-185.
[11]古平.基于贝叶斯模型的文档分类及相关技术研究[D].重庆:重庆大学计算机学院,2006.GU Ping.Research on Document Classification with Bayesian Model and Related Technologies[D].Chongqing:College of Computer Science,Chongqing University,2006.
[12]Breiman L.Bagging Predictors[J].Machine Learning,1996,24(2):123-140.
[13]Cortes C,Vapnik V.Support-Vector Networks[J].Machine Learning,1995,20(3):273-297.
[14]刘奇琦,龚晓峰.一种二值图像连通区域标记的新方1013法[J].计算机工程与应用,2012,48(11):178-180.LIU Qiqi,GONG Xiaofeng.New algorithm for binary connected component labeling[J].Computer Engineering and Applications,2012,48(11):178-180.
[15]徐利华,陈早生.二值图像中的游程编码区域标记[J].光电工程,2004,31(6):63-65.XU Lihua,CHEN Zaosheng.Region Labeling Based on Run-length Coding for Binary Image[J].Opto-Electronic Engineering,2004,31(6):63-65.
[16]Rafael C.Gonzalez,Richard E.Woods著.数字图像处理[M].第三版.阮秋琦等译.北京:电子工业出版社,2011:42-46.Rafael C.Gonzalez,Richard E.Woods Wrote.Digital Image Processing[M].Third Edition.RUAN Qiuqi etc.Translated.Beijing:Publishing House of Electronics Industry,2011:42-46.