利用小波分析对岩石图像分类
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- 英文论文题名：Classification of Rock Images by Wavelet Analysis
- 论文作者：王瑞祥
- 英文论文作者：Wang Rui-xiang ; The Third Engineering Co.Ltd.of the First Highway Engineering Bureau of cccc
- 年：2017
- 作者机构：中交一公局第三工程有限公司;
- 论文关键词：小波分析 ; 岩石图像分类 ; 频谱 ; 波段 ; 样本 ; 样本集 ; 聚类中心
- 英文论文关键词：Wavelet analysis ; Rock image classification ; Spectrum ; Band ; Sample ; Sample set ; Clustering center
- 会议召开时间：2017-07-01
- 会议录名称：2017年7月建筑科技与管理学术交流会论文集
- 语种：中文
- 分类号：TP391.41
- 学会代码：HSBY
- 会议名称：2017年7月建筑科技与管理学术交流会
- 会议地点：中国北京
- 主办单位：《建筑科技与管理》组委会
- 学会名称：北京恒盛博雅国际文化交流中心
- 页数：5
- 文件大小：1259k
- 原文格式：O
- 会议级别：全国
摘要
本文基于花岗岩和砂岩数字图像特征,利用小波分析理论及Bayes决策理论建立起岩石中几种成份(云母、石英、长石)的频谱图。首先利用岩石图像灰度统计函数存在多个极小值的特点,将其灰度级划分成若干个子区间,并利用迭代算法对区间进行优化,根据优化所得区间来建立起各类的样本集及其分布域。然后用小波理论对图像进行多重分解,按塔式原则将其各级系数矩阵还原成与原图像大小一致的矩阵,并对各矩阵进行均一化处理,经处理之后的小波系数矩阵为图像的波段。最后,以样本集为基样本,求出小波分解的各级分解系数与对应点的坐标集及其分解系数集,利用Bayes算法建立花岗岩和砂岩中各成份的频谱图。本文中频谱图是建立在先验基础之上的,在对频谱图的应用时,只需将一幅图片进行小波分解,同时对分解系数做还原及均一化处理,根据先验所得的频谱对样本进行计算,便可确定出被分析图像的各种成份及其分布情况。
Based on the characteristics of granite and sandstone digital images,this paper builds up the spectrum of several components(mica,quartz and feldspar) in rock by wavelet analysis theory and Bayes decision theory.Firstly,the gray level is divided into several subintervals by using the gray level statistical function of the rock image.The iterative algorithm is used to optimize the interval.According to the optimized range,the sample sets are set up.Its distribution domain.Then the wavelet is used to decompose the image,and the matrix of the coefficients is reduced to the same size as the original image according to the tower principle,and the matrix is processed uniformly.The wavelet coefficients matrix after processing is the band of the image The Finally,the spectral set of granite and sandstone is established by Bayes algorithm,and the spectral set of the decomposition coefficient of the wavelet decomposition and the corresponding coordinate set and its decomposition coefficient are obtained.In this paper,the spectrum is based on the transcendental basis,in the application of the spectrum,only a picture of the wavelet decomposition,while the decomposition factor to do the reduction and uniform processing,according to a priori spectrum pairs The samples are calculated to determine the various components of the image being analyzed and their distribution.
Based on the characteristics of granite and sandstone digital images,this paper builds up the spectrum of several components(mica,quartz and feldspar) in rock by wavelet analysis theory and Bayes decision theory.Firstly,the gray level is divided into several subintervals by using the gray level statistical function of the rock image.The iterative algorithm is used to optimize the interval.According to the optimized range,the sample sets are set up.Its distribution domain.Then the wavelet is used to decompose the image,and the matrix of the coefficients is reduced to the same size as the original image according to the tower principle,and the matrix is processed uniformly.The wavelet coefficients matrix after processing is the band of the image The Finally,the spectral set of granite and sandstone is established by Bayes algorithm,and the spectral set of the decomposition coefficient of the wavelet decomposition and the corresponding coordinate set and its decomposition coefficient are obtained.In this paper,the spectrum is based on the transcendental basis,in the application of the spectrum,only a picture of the wavelet decomposition,while the decomposition factor to do the reduction and uniform processing,according to a priori spectrum pairs The samples are calculated to determine the various components of the image being analyzed and their distribution.
引文
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[6]Seungcheol Shin,Roman D.Hryciw.Wavelet analysis of soil mass image for particle size determination.Journal of Computer in Civil Engineering,ASCE,2004,18;19-27.
[7]李建平.1997.小波分析与信号处理.重庆:重庆出版社.
[8]Lwin T,maritz JS.Empirical Bayes Approach to Multiparameter Estimation;with Special Reference to Multinomial Distribution.Ann.Inst.Statist.Math.1989,41(1):81~99.
[9]Edward O.Cannon·Ata Amíni·Andreas Bender,Michael J.E.Sternberg·Stephen H.Muggleton,Robert C.Glen John B.0.Mitchell.Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.J Cornput Aided Mol Des.2007,DOI 10.1007/s10822-007-9113-3.
[10]Peter Grunwald·John Langford.Suboptimal Behavior of Bayes and MDL in Classification under Misspecification.Mach Learn.2007,66:119~149.
[11]万建,王继成.基于ISODATA算法的彩色图像分割.计算机工程.2002,28(5):135~137.
[11]万建,王继成.基于ISODATA算法的彩色图像分割.计算机工程.
[13]F.Sik Yum Lee,Nian Sheng Tang.Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data.The Psychometric Society.2005,71(3):541~564.
[2]Lee JSJ,Haralick RM,Shapiro LG.Morphologic edge detection.IEEE Journal of Robtotics and Automation 1987;3:142~156.
[3]Zhao XB,Xu JM.Digital properties of geo-material images.In;Recent development and geo-environmental engineering in Asia,Proceeding of the 4th Asian Joint Symletsposium on Geotechnical and Geo-environmental Engineering,MT Luan,K Zen,GQ Chen,TK Nian,K Kasama,ed.Dalian;Dalian Univ.of Tech.Press,2006.pp.71-74.
[4]Hu RL,Yue ZQ,Tham LG,Wang LC.Digital image analysis of dynamic compaction effects on clay fills.Journal of Geotechnical and Geoenvironmental Engineering,ASCE,2005,131;1411~1422.
[5]周健,余荣传,贾敏才.基于数字图像技术的砂土模型试验细观结构参数测量.岩土工程学报.2006,28(12):2047~2052.
[6]Seungcheol Shin,Roman D.Hryciw.Wavelet analysis of soil mass image for particle size determination.Journal of Computer in Civil Engineering,ASCE,2004,18;19-27.
[7]李建平.1997.小波分析与信号处理.重庆:重庆出版社.
[8]Lwin T,maritz JS.Empirical Bayes Approach to Multiparameter Estimation;with Special Reference to Multinomial Distribution.Ann.Inst.Statist.Math.1989,41(1):81~99.
[9]Edward O.Cannon·Ata Amíni·Andreas Bender,Michael J.E.Sternberg·Stephen H.Muggleton,Robert C.Glen John B.0.Mitchell.Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.J Cornput Aided Mol Des.2007,DOI 10.1007/s10822-007-9113-3.
[10]Peter Grunwald·John Langford.Suboptimal Behavior of Bayes and MDL in Classification under Misspecification.Mach Learn.2007,66:119~149.
[11]万建,王继成.基于ISODATA算法的彩色图像分割.计算机工程.2002,28(5):135~137.
[11]万建,王继成.基于ISODATA算法的彩色图像分割.计算机工程.
[13]F.Sik Yum Lee,Nian Sheng Tang.Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data.The Psychometric Society.2005,71(3):541~564.