基于数字图像处理技术的爆堆粒度分析
|
摘要
爆堆岩石块度的研究是一直都是矿山开采中的一个重要问题，合理的爆破参数能够降低开采成本，减小二次破碎工作量，可以很大程度地提高开采效率。通常的块度分析法有筛分法，大块度统计法等人工测定法，但是这些方法都存在一定的缺陷。
基于计算机图像处理技术的单图片摄影测量法以及分形理论，通过分析获得的图像信息，提出了一种基于小波变换融合和创新的分水岭方法的图像分割技术，使得得到的分割图能够满足爆堆岩石块度的分布统计。
首先，根据现场情况确定了拍照方案，在采集到图像信息后，利用Photoshop 5软件对图像信息进行几何校正，并取得了不错的纠偏效果。其次，对于不同的两类图像分别采取了不同的图像处理方法。对于灰度不均匀的图像首先采用亮度反转处理和图像增强技术处理，然后再用小波变换进行融合；对于含有很多噪声的图像，首先对图像进行降噪处理，接着通过直方图修正方法增强图像，然后再利用小波融合技术得到理想的图像，这两种方法最终都得到了理想的结果。对经过图像处理的原始图像采用创新的分水岭方法进行图像分割，该创新方法是基于分别对图像的前景对象和背景对象进行区别标注的方法，最终获得正确的分割图。本文从两方面对爆堆岩石块度的分布进行了统计。一是从爆堆表面的实际面积大小和最大弦长方面来统计；二是根据综合运用单图片摄影测量法中的三种统计方法（面积法、线段法和体积法）来统计。
研究表明所采用的方法经济、快速地统计出了爆堆岩石块度的整体分布情况，并且具有对实际生产影响小，安全。
Rock blasting fragmentation analysis is very important in a mining indutry. Reasonable blasting parameter can reduce the costs of mining and lessen the subsequent workload, so that the overall efficiency in a mining industry can be greatly improved. Usually, the commonly used methods of fragmentation analysis are sieving, statistics on the rate of big block etc. All these methods have more or less limitations.
Based on digital image processing technology and the fractal theory, this paper proposes an image segmentation method based on wavelet resolution merge and watershed segmentation algorithm, to make sure that the segmented image is proper for rock blasting fragmentation analysis.
Firstly, Photograph scheme is determined according to the site condition, and the digital images information is taken. Photoshop 5 is used to geometrically correct the original digital images. For the original digital images, there are often two kinds of problems for rock blasting fragmentation analysis. One is unevenness of gray level in the image, and the other is the high level of noise in the image. For these two kinds of questionable images, different image processing methods are used to process the images to make sure that the processed images are suitable for rock fragmentation analysis. For uneven of gray level, brightness reversal and image enhancement techniques are used first, then wavelet resolution merge technique is used. For the image contained high level of noise, the noise is reduced first, and the histogram modification method is used to enhance the image. After that wavelet resolution merge technique is also used for the image with high level of noise. After the digital processing procedure, using either of the methods, the improved watershed segmentation algorithm is used to further process the digital images. The improved watershed segmentation method is proposed, based on correctly marking the objects on the foreground and background respectively, to properly segment the blocks in the digital images. Finally, the statistics are carried out on the digitally processed images by two statistical methods for blasting fragmentation analysis. In one method, the size of the rock block is calculated by the actual size and maximum string length from surface of the block in the digital image. In the other mothed, the size of the rock block is comprehensively appromated by three paramethers related with the size of a block, the area, length of a segment crossing the block and the volume.
It has been found that the proposed digital processing techniques are suitable for the rock fragmentation analysis in a digital image. It can be used for quick statistic on distribution of rock blasting fragmentation. It less affects the product work.
基于计算机图像处理技术的单图片摄影测量法以及分形理论，通过分析获得的图像信息，提出了一种基于小波变换融合和创新的分水岭方法的图像分割技术，使得得到的分割图能够满足爆堆岩石块度的分布统计。
首先，根据现场情况确定了拍照方案，在采集到图像信息后，利用Photoshop 5软件对图像信息进行几何校正，并取得了不错的纠偏效果。其次，对于不同的两类图像分别采取了不同的图像处理方法。对于灰度不均匀的图像首先采用亮度反转处理和图像增强技术处理，然后再用小波变换进行融合；对于含有很多噪声的图像，首先对图像进行降噪处理，接着通过直方图修正方法增强图像，然后再利用小波融合技术得到理想的图像，这两种方法最终都得到了理想的结果。对经过图像处理的原始图像采用创新的分水岭方法进行图像分割，该创新方法是基于分别对图像的前景对象和背景对象进行区别标注的方法，最终获得正确的分割图。本文从两方面对爆堆岩石块度的分布进行了统计。一是从爆堆表面的实际面积大小和最大弦长方面来统计；二是根据综合运用单图片摄影测量法中的三种统计方法（面积法、线段法和体积法）来统计。
研究表明所采用的方法经济、快速地统计出了爆堆岩石块度的整体分布情况，并且具有对实际生产影响小，安全。
Rock blasting fragmentation analysis is very important in a mining indutry. Reasonable blasting parameter can reduce the costs of mining and lessen the subsequent workload, so that the overall efficiency in a mining industry can be greatly improved. Usually, the commonly used methods of fragmentation analysis are sieving, statistics on the rate of big block etc. All these methods have more or less limitations.
Based on digital image processing technology and the fractal theory, this paper proposes an image segmentation method based on wavelet resolution merge and watershed segmentation algorithm, to make sure that the segmented image is proper for rock blasting fragmentation analysis.
Firstly, Photograph scheme is determined according to the site condition, and the digital images information is taken. Photoshop 5 is used to geometrically correct the original digital images. For the original digital images, there are often two kinds of problems for rock blasting fragmentation analysis. One is unevenness of gray level in the image, and the other is the high level of noise in the image. For these two kinds of questionable images, different image processing methods are used to process the images to make sure that the processed images are suitable for rock fragmentation analysis. For uneven of gray level, brightness reversal and image enhancement techniques are used first, then wavelet resolution merge technique is used. For the image contained high level of noise, the noise is reduced first, and the histogram modification method is used to enhance the image. After that wavelet resolution merge technique is also used for the image with high level of noise. After the digital processing procedure, using either of the methods, the improved watershed segmentation algorithm is used to further process the digital images. The improved watershed segmentation method is proposed, based on correctly marking the objects on the foreground and background respectively, to properly segment the blocks in the digital images. Finally, the statistics are carried out on the digitally processed images by two statistical methods for blasting fragmentation analysis. In one method, the size of the rock block is calculated by the actual size and maximum string length from surface of the block in the digital image. In the other mothed, the size of the rock block is comprehensively appromated by three paramethers related with the size of a block, the area, length of a segment crossing the block and the volume.
It has been found that the proposed digital processing techniques are suitable for the rock fragmentation analysis in a digital image. It can be used for quick statistic on distribution of rock blasting fragmentation. It less affects the product work.
引文
[1] G.C.Hunter, C.McDermott, N.J.Miles, A.Singh and M. J. Scoble. A Review of Image Analysis Techniques for Measuring Blast Fragmentation[J]. Mining Science and Technology, 1990(11):19-36.
[2] Kemeny, Jhon M. Practical Technique for Determining the Size Distribution of Blasted Benches,Waste Dumps and Heap Leach Sites[J]. Mining Engineering, 1994, 46(11):1281-1284.
[3]题正义,衣东丰.爆堆矿岩块度分布测试方法[J].辽宁工程技术大学学报,2003,22(增刊):1-3.
[4]题正义.爆堆块度分布的自动与分形测试系统[D].辽宁工程技术大学资源与环境工程学院,2002:3-10.
[5]郭学彬,肖正学.提高石灰石矿穿爆效果的技术研究[J].中国矿业,2000,9(1):41-45.
[6]题正义.露天矿爆破后煤岩块分布测试方法综述[J].阜新矿业学报(自然科学版),1994,13(2):38-43.
[7] D巴哈,Y奥斯基利克.利用大爆破后现场测量结果预测破碎效果[J].国外金属矿山炸药与爆破,1999(1):36-40.
[8]李悦樨,范方忠.用垂直摄影法测定爆堆块度分布[J].中国有色金属学报,1992,2(4):6-9.
[9] Kemeny,Jhon Metal. Analysis of rock fragmentation using digital image processing[J]. Journal of Geotechnical Engineering,1993,119(7):1144-1160.
[10]宋克英,刑占利等.图像处理技术在矿岩统计中的应用[J].矿业快报,2006(1):26-28..
[11] J Kemeny, E Mofya, R Kaunda and P Lever. Improvements in Blast Fragmentation Models Using Figital Image Processing[J]. Hunter Valley, 2002, 6(3-4):28-31.
[12] J.A. Sanchidrian, P. Segarra and L. M. Lopez. A practical Procedure for the Measurement of Fragmentation by Blasting by Iimage Analysis[J]. Rock Mechanics and Rock Engineering, 2006, 39(4):359-382.
[13] J A Sanchidrian, P Segarra and L M Lopez. On the Accuracy of Fragment Size Measurement by Image Analysis in Combination with some DistributionFunctions[J]. Rock Mechanics and Rock Engineering, 2009, 42(1):95-116.
[14]题正义.单图片摄影测试系统的煤岩块度分布计算方法[J].阜新矿业学院党报(自然科学版),1994,13(1):14-16.
[15]阮秋琦.数字图像处理学[M].北京,电子工业出版社,2001:11-17.
[16]孙臣良,题正义等.基于图像处理的爆堆块度分布自动测试系统[J].辽宁工程技术大学学报,2002,21(4):449-451.
[17]余仁兵,张红军等.爆破块度计算的CAD处理及应用[J].西部探矿工程,2007(3):96-100.
[18] Hong-Gyoo Sohn, Konghyun Yun and Hoon Chang. Analysis of Image Fusion Methods Using IKONOS Imagery[J]. KSCE Journal of Civil Engineering, 2003, 7(5):577-584.
[19] Hardy A J, Kemeny J M. Block Size Distribution of Rock Masses Using Digital Image Processing of Drill Core[J]. International Joural of Rock Mechanics and Mining Sciences, 1997, 34(2):303-307.
[20]傅洪贤,张幼蒂.爆破粒度的图像处理技术及其优化控制模型[J].矿业安全与环保,2001,28(2):8-9.
[21]杨军,王树仁.岩石爆破块度分布的分形研究初探[J].岩石破碎理论与实践,1992(9):370-375.
[22]谭英显.露天矿爆堆粒度分布的测定研究[J].矿山技术,1992(1):33-37.
[23]郭连军,黄国君,谭应显.倾斜摄影法测定爆堆表面矿岩块度分布的误差分析[C].岩石破碎理论与实践——全国第五届岩石破碎学术会论文选,1992(9):394-397.
[24]林大泽.爆堆块度评价方法研究的进展[J].中国安全科学学报,2003,13(9):9-13.
[25] Silander, J. Zhou D. L. Civco J. A. A Wavelet Transform Method to Merge Landsat TM and SPOT Panchromatic Data[J]. International Journal of Remote Sensing, 1998, 19(4):743-757.
[26]杨帆.数字图像处理与分析[M].北京:北京航空航天大学出版社,2010:1-10.
[27]常青编.数字图像处理教程[M].上海:华东理工出版社,2009:16-18.
[28]张德丰.详解MATLAB数字图像处理[M].北京:电子工业出版社,2010:249-260.
[29]蒋志勇,陈晓铃,秦前清.一种基于重构算子的分水岭变换算法[J].中国图像图形学报,2009,14(12):2527-2531.
[30]周宇峰,程景全.小波变换及其应用[J].物理学和高新技术,2008,37(1):24-32.
[31]秦襄培编著. MATLAB图像处理与界面编程宝典[M].北京:电子工业出版社,2009:386-403.
[32]曾世奇.露天矿爆堆矿岩块度分布测定的摄影—图像分析法[J].有色金属,1988,40(2):22-29.
[33] A.G. Hanbury and J. Serra. Morphological Operators on the Unit Circle[J]. Image Process, 2001, 10(12):1842–1850.
[34] Joseph Yossi Gil, Ron Kimmel. Efficient Dilation,Erosion,Opening and Closing Algorithms[J]. IEEE, 2002,24(12):1606-1617.
[35]尹文琴,刘前进.数学形态学在电力系统中的应用综述[J].继电器,2007,35(19):76-79.
[36]尹星云.数学形态学的基本原理和发展[J].科技创新导报,2008,1(28):184.
[37]贺兴华. MATLAB7.x图像处理[M].北京:人民邮电出版社,2006:195-196.
[38]刘金国.大视场光电测量系统的精密几何标定和畸变校正的研究[M].光学精密工程,1994,8(1):38-40.
[39] A Velmurugan, Guillen G Carlos. Soil Resource Assessment and Mapping using Remote Sensing and GIS[J]. Journal of the Indian Society of Remote Sensing, 2009, 37(3):511-525.
[40]杨昕. ERDAS遥感数字图像处理实验教程[M].北京:科学出版社,2009:122-123.
[41]党安荣. ERDAS遥感数字图像处理教程[M].北京:清华大学出版社,2010:82-83.
[42] Abdallah K Cherri and Mohammad A Karm. Optical Symbolic Substitution:Edge Detection Using Prewitt,Sobel,and Roberts operators[J]. Applied Optics, 1989, 28(21):4644-4648.
[43] Y Liu, K N Ngan. Fast Multiresolution Motion Estimation Algorithms for Wavelet-based Scalable Video Coding[J]. Signal Process:Image Commun, 2007, 22(5):448–465.
[44] P. Soille. Morphological Partitioning of Multispectral Images[J]. J. Electron. Imaging, 1996, 5(3):252–265.
[45]张德丰. MATLAB数字图像处理[M].北京:机械工业出版社,2009:179-184.
[46] Mahyari, A. G. Yazdi, M. Panchromatic and Multispectral Image Fusion Based on Maximization of Both Spectral and Spatial Similarities[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2011, 49(1):1976– 1985.
[47] M. Pesaresi and J.A. Benediktsson. A New Approach for the Morphological Segmentation of High-resolution Satellite Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 65(2):309–320.
[48] Weidong Lei,Kai Li and Xueping Wang. Improved Watershed Segmentation Method in Rock Fragmentation Analysis on Digital Photos[C]. Advanced Materials Research, 2011, 261-263:1734-1737.
[49] G. Noyel, J. Angulo and D. Jeulin. Morphological Segmentation of Hyperspectral Images[J]. Image Analysis and Stereology, 2007, 50(1):101–109.
[50] J. Angulo, J. Serra. Morphological Coding of Color Images by Vector Connected Filters[J]. Proceedings of ISSPA, 2003(1):69–72.
[2] Kemeny, Jhon M. Practical Technique for Determining the Size Distribution of Blasted Benches,Waste Dumps and Heap Leach Sites[J]. Mining Engineering, 1994, 46(11):1281-1284.
[3]题正义,衣东丰.爆堆矿岩块度分布测试方法[J].辽宁工程技术大学学报,2003,22(增刊):1-3.
[4]题正义.爆堆块度分布的自动与分形测试系统[D].辽宁工程技术大学资源与环境工程学院,2002:3-10.
[5]郭学彬,肖正学.提高石灰石矿穿爆效果的技术研究[J].中国矿业,2000,9(1):41-45.
[6]题正义.露天矿爆破后煤岩块分布测试方法综述[J].阜新矿业学报(自然科学版),1994,13(2):38-43.
[7] D巴哈,Y奥斯基利克.利用大爆破后现场测量结果预测破碎效果[J].国外金属矿山炸药与爆破,1999(1):36-40.
[8]李悦樨,范方忠.用垂直摄影法测定爆堆块度分布[J].中国有色金属学报,1992,2(4):6-9.
[9] Kemeny,Jhon Metal. Analysis of rock fragmentation using digital image processing[J]. Journal of Geotechnical Engineering,1993,119(7):1144-1160.
[10]宋克英,刑占利等.图像处理技术在矿岩统计中的应用[J].矿业快报,2006(1):26-28..
[11] J Kemeny, E Mofya, R Kaunda and P Lever. Improvements in Blast Fragmentation Models Using Figital Image Processing[J]. Hunter Valley, 2002, 6(3-4):28-31.
[12] J.A. Sanchidrian, P. Segarra and L. M. Lopez. A practical Procedure for the Measurement of Fragmentation by Blasting by Iimage Analysis[J]. Rock Mechanics and Rock Engineering, 2006, 39(4):359-382.
[13] J A Sanchidrian, P Segarra and L M Lopez. On the Accuracy of Fragment Size Measurement by Image Analysis in Combination with some DistributionFunctions[J]. Rock Mechanics and Rock Engineering, 2009, 42(1):95-116.
[14]题正义.单图片摄影测试系统的煤岩块度分布计算方法[J].阜新矿业学院党报(自然科学版),1994,13(1):14-16.
[15]阮秋琦.数字图像处理学[M].北京,电子工业出版社,2001:11-17.
[16]孙臣良,题正义等.基于图像处理的爆堆块度分布自动测试系统[J].辽宁工程技术大学学报,2002,21(4):449-451.
[17]余仁兵,张红军等.爆破块度计算的CAD处理及应用[J].西部探矿工程,2007(3):96-100.
[18] Hong-Gyoo Sohn, Konghyun Yun and Hoon Chang. Analysis of Image Fusion Methods Using IKONOS Imagery[J]. KSCE Journal of Civil Engineering, 2003, 7(5):577-584.
[19] Hardy A J, Kemeny J M. Block Size Distribution of Rock Masses Using Digital Image Processing of Drill Core[J]. International Joural of Rock Mechanics and Mining Sciences, 1997, 34(2):303-307.
[20]傅洪贤,张幼蒂.爆破粒度的图像处理技术及其优化控制模型[J].矿业安全与环保,2001,28(2):8-9.
[21]杨军,王树仁.岩石爆破块度分布的分形研究初探[J].岩石破碎理论与实践,1992(9):370-375.
[22]谭英显.露天矿爆堆粒度分布的测定研究[J].矿山技术,1992(1):33-37.
[23]郭连军,黄国君,谭应显.倾斜摄影法测定爆堆表面矿岩块度分布的误差分析[C].岩石破碎理论与实践——全国第五届岩石破碎学术会论文选,1992(9):394-397.
[24]林大泽.爆堆块度评价方法研究的进展[J].中国安全科学学报,2003,13(9):9-13.
[25] Silander, J. Zhou D. L. Civco J. A. A Wavelet Transform Method to Merge Landsat TM and SPOT Panchromatic Data[J]. International Journal of Remote Sensing, 1998, 19(4):743-757.
[26]杨帆.数字图像处理与分析[M].北京:北京航空航天大学出版社,2010:1-10.
[27]常青编.数字图像处理教程[M].上海:华东理工出版社,2009:16-18.
[28]张德丰.详解MATLAB数字图像处理[M].北京:电子工业出版社,2010:249-260.
[29]蒋志勇,陈晓铃,秦前清.一种基于重构算子的分水岭变换算法[J].中国图像图形学报,2009,14(12):2527-2531.
[30]周宇峰,程景全.小波变换及其应用[J].物理学和高新技术,2008,37(1):24-32.
[31]秦襄培编著. MATLAB图像处理与界面编程宝典[M].北京:电子工业出版社,2009:386-403.
[32]曾世奇.露天矿爆堆矿岩块度分布测定的摄影—图像分析法[J].有色金属,1988,40(2):22-29.
[33] A.G. Hanbury and J. Serra. Morphological Operators on the Unit Circle[J]. Image Process, 2001, 10(12):1842–1850.
[34] Joseph Yossi Gil, Ron Kimmel. Efficient Dilation,Erosion,Opening and Closing Algorithms[J]. IEEE, 2002,24(12):1606-1617.
[35]尹文琴,刘前进.数学形态学在电力系统中的应用综述[J].继电器,2007,35(19):76-79.
[36]尹星云.数学形态学的基本原理和发展[J].科技创新导报,2008,1(28):184.
[37]贺兴华. MATLAB7.x图像处理[M].北京:人民邮电出版社,2006:195-196.
[38]刘金国.大视场光电测量系统的精密几何标定和畸变校正的研究[M].光学精密工程,1994,8(1):38-40.
[39] A Velmurugan, Guillen G Carlos. Soil Resource Assessment and Mapping using Remote Sensing and GIS[J]. Journal of the Indian Society of Remote Sensing, 2009, 37(3):511-525.
[40]杨昕. ERDAS遥感数字图像处理实验教程[M].北京:科学出版社,2009:122-123.
[41]党安荣. ERDAS遥感数字图像处理教程[M].北京:清华大学出版社,2010:82-83.
[42] Abdallah K Cherri and Mohammad A Karm. Optical Symbolic Substitution:Edge Detection Using Prewitt,Sobel,and Roberts operators[J]. Applied Optics, 1989, 28(21):4644-4648.
[43] Y Liu, K N Ngan. Fast Multiresolution Motion Estimation Algorithms for Wavelet-based Scalable Video Coding[J]. Signal Process:Image Commun, 2007, 22(5):448–465.
[44] P. Soille. Morphological Partitioning of Multispectral Images[J]. J. Electron. Imaging, 1996, 5(3):252–265.
[45]张德丰. MATLAB数字图像处理[M].北京:机械工业出版社,2009:179-184.
[46] Mahyari, A. G. Yazdi, M. Panchromatic and Multispectral Image Fusion Based on Maximization of Both Spectral and Spatial Similarities[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2011, 49(1):1976– 1985.
[47] M. Pesaresi and J.A. Benediktsson. A New Approach for the Morphological Segmentation of High-resolution Satellite Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 65(2):309–320.
[48] Weidong Lei,Kai Li and Xueping Wang. Improved Watershed Segmentation Method in Rock Fragmentation Analysis on Digital Photos[C]. Advanced Materials Research, 2011, 261-263:1734-1737.
[49] G. Noyel, J. Angulo and D. Jeulin. Morphological Segmentation of Hyperspectral Images[J]. Image Analysis and Stereology, 2007, 50(1):101–109.
[50] J. Angulo, J. Serra. Morphological Coding of Color Images by Vector Connected Filters[J]. Proceedings of ISSPA, 2003(1):69–72.