基于纹理分析的煤与非煤物的图像识别算法研究
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摘要
煤炭产量、运量控制系统对于解决煤炭生产行业架构的特殊性造成的难以控管的局面,实现煤炭产业的现代化管理具有重要意义。本文主要研究产运煤源点(生产矿)、结算站和储煤场的煤炭运量控制系统中的车载物(煤和非煤物)识别系统,即利用数字图像处理技术实现煤与非煤物自动识别,在此过程中所应用的技术和方法为今后的深入研究和将其应用于实际生产奠定了坚实的基础。
本文所研究的煤与非煤物的识别是基于产运煤源点(生产矿)、结算站和储煤场这些特定的应用环境中,研究的是散煤与非煤物的识别。通过研究、分析煤与非煤物的图像,和大量的基于灰度共生矩阵的煤与非煤物的纹理特征数据,总结出了描述煤的纹理特征向量,通过支持向量机对图像样本进行训练和分类,并得到了良好的识别效果。每一种物质都具有自己特有的纹理特征,利用计算机图像处理技术完成煤与非煤物纹理特征的计算,可以获得我们需要的纹理特征向量,通过支持向量机对所获特征向量进行分类就可以判断所识别图像是煤或者非煤。
本文提出了一种满足煤炭运量系统要求的煤与非煤物的图像识别算法：基于灰度共生矩阵的纹理分析识别算法的改进算法,在纹理特征中加入了灰度特征,提高了识别率。并且通过对灰度共生矩阵的可调参数的讨论与选择,克服了灰度共生矩阵对图像尺度变化敏感的缺点。
Coal output and transport volume control system is great significance for solving the situation that the management of coal production industry is difficult and realizing the modernization management of the coal industry. In this paper, recognition of stowage(coal and non-coal) in coal transport volume control system of sources of coal production and transportation (ore production)、settlement station and coal storage yard was studied. Digital image processing technology was used to study and achieve recognition algorithm of coal and non-coal automatic identification. The techniques and methods in this paper lay a solid foundation for further researching and application
In this paper, coal and non-coal recognition was different from the previous coal and non-coal separation recognition system. It was based on the specific application environments of coal production and transportation (ore production)、settlement station and coal storage yard. Through the research and analysis of coal and non-coal image, and research and analysis the data of coal and non-coal textural property based on Gray-Level Co-Occurrence Matrix, summing up the coal grain property eigenvector. Through training and classification of the sample image by SVM, in this way can get a well recognize effect. Every material have its own textural property, make use of computer image processing technologies to finish the compute of coal and non-coal textural property, we can get the required grain property vector. We can judge the material if it is coal by classified the eigenvector got by SVM.
The recognize method in this paper realized automatic recognition of coal and non-coal in coal yard. The experimental proof that we get the eigenvector based on image gray level co-occurrence matrix can describe the coal feature exactly and recognize the coal and non-coal.
本文所研究的煤与非煤物的识别是基于产运煤源点(生产矿)、结算站和储煤场这些特定的应用环境中,研究的是散煤与非煤物的识别。通过研究、分析煤与非煤物的图像,和大量的基于灰度共生矩阵的煤与非煤物的纹理特征数据,总结出了描述煤的纹理特征向量,通过支持向量机对图像样本进行训练和分类,并得到了良好的识别效果。每一种物质都具有自己特有的纹理特征,利用计算机图像处理技术完成煤与非煤物纹理特征的计算,可以获得我们需要的纹理特征向量,通过支持向量机对所获特征向量进行分类就可以判断所识别图像是煤或者非煤。
本文提出了一种满足煤炭运量系统要求的煤与非煤物的图像识别算法：基于灰度共生矩阵的纹理分析识别算法的改进算法,在纹理特征中加入了灰度特征,提高了识别率。并且通过对灰度共生矩阵的可调参数的讨论与选择,克服了灰度共生矩阵对图像尺度变化敏感的缺点。
Coal output and transport volume control system is great significance for solving the situation that the management of coal production industry is difficult and realizing the modernization management of the coal industry. In this paper, recognition of stowage(coal and non-coal) in coal transport volume control system of sources of coal production and transportation (ore production)、settlement station and coal storage yard was studied. Digital image processing technology was used to study and achieve recognition algorithm of coal and non-coal automatic identification. The techniques and methods in this paper lay a solid foundation for further researching and application
In this paper, coal and non-coal recognition was different from the previous coal and non-coal separation recognition system. It was based on the specific application environments of coal production and transportation (ore production)、settlement station and coal storage yard. Through the research and analysis of coal and non-coal image, and research and analysis the data of coal and non-coal textural property based on Gray-Level Co-Occurrence Matrix, summing up the coal grain property eigenvector. Through training and classification of the sample image by SVM, in this way can get a well recognize effect. Every material have its own textural property, make use of computer image processing technologies to finish the compute of coal and non-coal textural property, we can get the required grain property vector. We can judge the material if it is coal by classified the eigenvector got by SVM.
The recognize method in this paper realized automatic recognition of coal and non-coal in coal yard. The experimental proof that we get the eigenvector based on image gray level co-occurrence matrix can describe the coal feature exactly and recognize the coal and non-coal.
引文
1.蒋勇.数字图像处理技术在煤矸石自动分选系统中的应用[D],西安科技大学学位论文,2004
2.刘文华.煤矸混合物料选择性破碎分选的研究[D],太原：太原理工大学,2003
3.张剑英.煤矿多媒体图像监控系统[J],煤炭科学技术,1997,25(6)：32-34,
4.姚敏.数字图像处理[M],北京：机械工业出版社,2006,1(1)：1-8
5.吴健康.数字图像分析[M],北京：清华大学出版社.1989年.
6. Rafael C. Gonzales Richard E. Woods[美]等著,阮秋琦,阮宇智等译.数字图像处理(第二版)[M],北京：电子工业出版社,2003
7.黄贤武,王加俊.数字图像处理与压缩编码技术[M],电子科技大学出版社,2000
8.贾永红.计算机图像处理与分析[M],武汉大学出版社,2001
9.胡小锋,赵辉.Visual C++/Matlab图像处理与识别实用案例精选[M],北京：人民邮电出版社,2004
10.赵荣椿.数字图像处理导论[M],西北工业大学出版社,1995
11.李文锋.图形图像处理与应用,第一版[M],中国标准出版社,2006
12.边肇祺,张学工.模式识别[M],北京：清华大学出版社,1999
13.章毓晋.基于内容的视觉信息检索[M],北京：科学出版社,2003
14.蔡涛.面向目标识别的图像特征融合提取技术研究[D],工学博士学位论文.国防科学技术大学,2000
15.季虎,孙即祥,邵晓芳,毛玲.图像边缘提取方法与展望[J],计算机工程与应用,2004,14：70-73
16. S Prabhakar, M R Henderson. Automatic Form-Feature Recognition Using Neural network-based Techniques on Boundary Representation of Solid Models[J]. Computer-Aided Design,1992,24(7):381-393
17.冉向书,任金昌.图像融合中的特征选取及分类与决策[J],计算机应用,2005,09：170-173
18.D.S Zhang, G.J.Lu, Evaluation of similarity measurement for image retrieval[C]. IEEE Int. Conf. Neural Networks and Signal Processing,Nan Jing, China,2003,928—931
19. H.Y.Lee, H.K.Lee, Y.H.Ha, Spatial color descriptor for image retrieval and video segmentation[J], IEEE Transactions on Multimedia,2003,5(3):358-367
20. V.Monga, B.L.Evans, Perceptual image hashing via feature points:performance evaluation and trade-offs[J], IEEE Transactions on Image Processing,2006,15(11):3453-3466
21. A.Swaminathan, Y.Mao, M.Wu, Robust and secure image hashing[J], IEEE Transactions on Information Forensics and Security,2006,1(2):215-230
22. S.Wang, X.Lu, S.Su, X.Zhang, Image Block Feature Vectors Based on a Singular-Value Information Metric and Color-Texture Description[J], Journal of Shanghai University, 2007,11(3):205-209。
23.王娟,慈林林,姚康泽.特征选择方法综述[J],计算机工程与科学,2005,27(12)：68-70
24.王健,靳奉祥,史玉峰.模式识别中特征选择与聚类分析[J],测绘工程,2002,1(2)：21-24
25.夏庆观,温秀兰,盛党红.零件多源图像特征提取和识别的研究[J],机械设计与制造2006,07：48-50
26. J A Benediktsson, J R Sveinsson. Feature Extraction for Multisource Data Classification with Artificial Neural Networks[J]. Int.J.Remote Sensing,1997,18(4)
27.李江,程建等.数字图像处理中多窗口下的自适应中值滤波[J],计算机工程.2003,29(17)：154-156
28. Sluzek A.Identification and inspecton of 2D objects using new moment-based shape descriptors[J]. Pattern Recognition Letter,1995,16(3):687-697
29. R.M.Haralick, LG.Shapiro, Computer and Robot Vision[M],1992, Addison-Wesley
30. H.Tumara, S.Mori, T.Yamawaki, Texture features corresponding to visual perception[M], IEEE, SMC1978,8(6):460-473
31.B.S.Manjunath, W.Y.Ma,Texture features for browsing and retrieval of image data[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):837-842
32. D.Michael, Wavelet features for color image classification[C], Imaging and Geospatial Information Society, Oralando,2000
33.汪祖嫒,粱椽,李斌.基于树状小波分辨的纹理图像检索[J],中国图象图形学报,2001,6A(11)：1065-1069
34. C.Faloutsos, R.Barber, M.Flickner,et al, Efficient and effective querying by image content[J], Intelligent information systems,1994,3(1):231-262
35.L.Fudos, L.Patios, An efficient shape-based approach to image retrieval[J].PRL,2002, 23(6):731-741
36.石军,常义林.图像检索技术综述[J],西安电子科技大学学报(自然科学版),2003,30(4)：486-491
37.曾志强,高济,谢彦麒.基于聚类的快速支持向量机训练算法[J],现代计算机,2008,9：57-59
38. Chih-Chung Chang. A Library for Support Vector Ma-chines,2004
39.刘硕明,刘佳,杨海滨.一种新的多类支持向量机算法[J],计算机应用,2008,12(28)：86-88
40.艾扬利,赵忠芹,杨兵.基于新核函数的支持向量机在物体分类中的应用[J],中国测试技术,2008,34(1)：80-83
41.张晓龙,邱泽伟,张晓芳.基于多目标优化的SVM多类分类方法[J],计算机工程与设计,2009,8：78-79
42.刘浩翰,于洋.汽车牌照识别系统中关键算法的设计与实现[J],计算机应用,2007,27(B06)：121-124
43.吕一波,彭德强.利用图像识别技术提取粗粒煤图像的物理特征[J],洁净煤技术,2006,12(3)：98-100
44.王鲁敏.基于图像分析方法的颗粒粒度测量[J],化工装备技术,26(4)：65-67,2005
45.于海鹏,刘一星,刘镇波.基于图像纹理特征的木材树种识别[J],林业科学,2007,43(4)：77-81
46. Juan Martinez, Jose D.Luis-Delgado, Luis M.Tomas-Balibrea, Automatic system forquality-based classification of marble textures[J], IEEE Transactions On Systems, Man, And Cybernetics-oart C:Applications And Reviews,2005,35(4):488-497
47. B.Kimia, Shape representation for image retrieval[M]. Image Database-Search and Retrieval of Digital Imagery. Castelli V, Bergman LD,eds. John Wiley&Sons,2002, Inc,Ch.13,345-372
48.郑扬,俞能海.基于共生矩阵的SAR图像村庄识别方法[J],计算机仿真,2008,3：98-100
49.马宪民,蒋勇.煤矸石二值图像的Roberts快速边缘检测法[J],仪器仪表学报,2005,26(8)：595-597
50.马宪民.一种基于小波变换的煤矸石图像边缘检测方法[J],仪器仪表学报,2006,27(6)：2130-2131
51.马宪民,蒋勇.煤与矸石识别的数字图像处理方法探讨[J],煤矿机电,2004(5)：9-11
52.李楠,闵亮.一种新的纹理图像识别方法[J],科技信息,2008,34：56-57
53. Robert Alterson. Mitras Spetsakis. Object recognition with adaptive Gabor features[J], Image and Vision Computing,2004,22:1007-1014
54.和文全,薛惠峰,解丹蕊,杜喆.基于K近邻的支持向量机分类方法[J],计算机仿真,2008,11：57-59
2.刘文华.煤矸混合物料选择性破碎分选的研究[D],太原：太原理工大学,2003
3.张剑英.煤矿多媒体图像监控系统[J],煤炭科学技术,1997,25(6)：32-34,
4.姚敏.数字图像处理[M],北京：机械工业出版社,2006,1(1)：1-8
5.吴健康.数字图像分析[M],北京：清华大学出版社.1989年.
6. Rafael C. Gonzales Richard E. Woods[美]等著,阮秋琦,阮宇智等译.数字图像处理(第二版)[M],北京：电子工业出版社,2003
7.黄贤武,王加俊.数字图像处理与压缩编码技术[M],电子科技大学出版社,2000
8.贾永红.计算机图像处理与分析[M],武汉大学出版社,2001
9.胡小锋,赵辉.Visual C++/Matlab图像处理与识别实用案例精选[M],北京：人民邮电出版社,2004
10.赵荣椿.数字图像处理导论[M],西北工业大学出版社,1995
11.李文锋.图形图像处理与应用,第一版[M],中国标准出版社,2006
12.边肇祺,张学工.模式识别[M],北京：清华大学出版社,1999
13.章毓晋.基于内容的视觉信息检索[M],北京：科学出版社,2003
14.蔡涛.面向目标识别的图像特征融合提取技术研究[D],工学博士学位论文.国防科学技术大学,2000
15.季虎,孙即祥,邵晓芳,毛玲.图像边缘提取方法与展望[J],计算机工程与应用,2004,14：70-73
16. S Prabhakar, M R Henderson. Automatic Form-Feature Recognition Using Neural network-based Techniques on Boundary Representation of Solid Models[J]. Computer-Aided Design,1992,24(7):381-393
17.冉向书,任金昌.图像融合中的特征选取及分类与决策[J],计算机应用,2005,09：170-173
18.D.S Zhang, G.J.Lu, Evaluation of similarity measurement for image retrieval[C]. IEEE Int. Conf. Neural Networks and Signal Processing,Nan Jing, China,2003,928—931
19. H.Y.Lee, H.K.Lee, Y.H.Ha, Spatial color descriptor for image retrieval and video segmentation[J], IEEE Transactions on Multimedia,2003,5(3):358-367
20. V.Monga, B.L.Evans, Perceptual image hashing via feature points:performance evaluation and trade-offs[J], IEEE Transactions on Image Processing,2006,15(11):3453-3466
21. A.Swaminathan, Y.Mao, M.Wu, Robust and secure image hashing[J], IEEE Transactions on Information Forensics and Security,2006,1(2):215-230
22. S.Wang, X.Lu, S.Su, X.Zhang, Image Block Feature Vectors Based on a Singular-Value Information Metric and Color-Texture Description[J], Journal of Shanghai University, 2007,11(3):205-209。
23.王娟,慈林林,姚康泽.特征选择方法综述[J],计算机工程与科学,2005,27(12)：68-70
24.王健,靳奉祥,史玉峰.模式识别中特征选择与聚类分析[J],测绘工程,2002,1(2)：21-24
25.夏庆观,温秀兰,盛党红.零件多源图像特征提取和识别的研究[J],机械设计与制造2006,07：48-50
26. J A Benediktsson, J R Sveinsson. Feature Extraction for Multisource Data Classification with Artificial Neural Networks[J]. Int.J.Remote Sensing,1997,18(4)
27.李江,程建等.数字图像处理中多窗口下的自适应中值滤波[J],计算机工程.2003,29(17)：154-156
28. Sluzek A.Identification and inspecton of 2D objects using new moment-based shape descriptors[J]. Pattern Recognition Letter,1995,16(3):687-697
29. R.M.Haralick, LG.Shapiro, Computer and Robot Vision[M],1992, Addison-Wesley
30. H.Tumara, S.Mori, T.Yamawaki, Texture features corresponding to visual perception[M], IEEE, SMC1978,8(6):460-473
31.B.S.Manjunath, W.Y.Ma,Texture features for browsing and retrieval of image data[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):837-842
32. D.Michael, Wavelet features for color image classification[C], Imaging and Geospatial Information Society, Oralando,2000
33.汪祖嫒,粱椽,李斌.基于树状小波分辨的纹理图像检索[J],中国图象图形学报,2001,6A(11)：1065-1069
34. C.Faloutsos, R.Barber, M.Flickner,et al, Efficient and effective querying by image content[J], Intelligent information systems,1994,3(1):231-262
35.L.Fudos, L.Patios, An efficient shape-based approach to image retrieval[J].PRL,2002, 23(6):731-741
36.石军,常义林.图像检索技术综述[J],西安电子科技大学学报(自然科学版),2003,30(4)：486-491
37.曾志强,高济,谢彦麒.基于聚类的快速支持向量机训练算法[J],现代计算机,2008,9：57-59
38. Chih-Chung Chang. A Library for Support Vector Ma-chines,2004
39.刘硕明,刘佳,杨海滨.一种新的多类支持向量机算法[J],计算机应用,2008,12(28)：86-88
40.艾扬利,赵忠芹,杨兵.基于新核函数的支持向量机在物体分类中的应用[J],中国测试技术,2008,34(1)：80-83
41.张晓龙,邱泽伟,张晓芳.基于多目标优化的SVM多类分类方法[J],计算机工程与设计,2009,8：78-79
42.刘浩翰,于洋.汽车牌照识别系统中关键算法的设计与实现[J],计算机应用,2007,27(B06)：121-124
43.吕一波,彭德强.利用图像识别技术提取粗粒煤图像的物理特征[J],洁净煤技术,2006,12(3)：98-100
44.王鲁敏.基于图像分析方法的颗粒粒度测量[J],化工装备技术,26(4)：65-67,2005
45.于海鹏,刘一星,刘镇波.基于图像纹理特征的木材树种识别[J],林业科学,2007,43(4)：77-81
46. Juan Martinez, Jose D.Luis-Delgado, Luis M.Tomas-Balibrea, Automatic system forquality-based classification of marble textures[J], IEEE Transactions On Systems, Man, And Cybernetics-oart C:Applications And Reviews,2005,35(4):488-497
47. B.Kimia, Shape representation for image retrieval[M]. Image Database-Search and Retrieval of Digital Imagery. Castelli V, Bergman LD,eds. John Wiley&Sons,2002, Inc,Ch.13,345-372
48.郑扬,俞能海.基于共生矩阵的SAR图像村庄识别方法[J],计算机仿真,2008,3：98-100
49.马宪民,蒋勇.煤矸石二值图像的Roberts快速边缘检测法[J],仪器仪表学报,2005,26(8)：595-597
50.马宪民.一种基于小波变换的煤矸石图像边缘检测方法[J],仪器仪表学报,2006,27(6)：2130-2131
51.马宪民,蒋勇.煤与矸石识别的数字图像处理方法探讨[J],煤矿机电,2004(5)：9-11
52.李楠,闵亮.一种新的纹理图像识别方法[J],科技信息,2008,34：56-57
53. Robert Alterson. Mitras Spetsakis. Object recognition with adaptive Gabor features[J], Image and Vision Computing,2004,22:1007-1014
54.和文全,薛惠峰,解丹蕊,杜喆.基于K近邻的支持向量机分类方法[J],计算机仿真,2008,11：57-59