基于彩色线阵CCD大米色选算法实验研究
|
摘要
由于色选机独特而有效筛选方式，它在大米等粮食筛选加工工业中得到了广泛的应用。目前我国的颜色分选机研制技术与国外产品相比还有差距，很多厂家需要靠进口昂贵的国外产品。因此作为一个农业大国，研制适合我国国情的颜色分选机，并广泛应用于粮食及产品加工多种行业，将会带来巨大的经济效益和社会效益。
本文根据色选机的色选过程，利用PCI数据采集卡及彩色线阵CCD摄像头，搭建了一个图像数据采集实验装置。通过VB编写的控制界面操作该装置，实现对不同大米线阵图像数据的采集，并将采集的数据以文本文档的形式储存，作为后续大米色选算法研究的数据源。
通过对每一帧图像数据的研究与分析，发现图像数据中的高频噪声对于提取大米异色粒的特征信息产生较大影响，为了减小或消除高频噪声的干扰，本文尝试用小波滤波的处理方法以得到平滑的信号。并根据数字图像处理中特征提取的相关理论，结合实际图像数据特征，本文采用k-均值聚类分析方法将图像数据分为三类：背景类、正常米类和异色米类。实现从图像数据中提取异色点的目的。
与现有色选算法原理不同，该算法不需要人为操作或提供某种先验值，而是由聚类分析算法根据输入数据的内部特征，直接确定异色米所在的类。
最后，本文利用对20组不同大米采集的2000多帧图像数据，进行算法验证，从整个实验的结果和分析可知该算法是实际有效的。
Because of its unique and effective screening method, color sorting apparatus is widely used in sorting and processing industry of rice and other foodstuff. At present, our country lags behind some foreign countries in the technology of color sorting apparatus, most manufacturers have to rely on expensive imports of foreign products. Therefore, for china, a large agricultural country, it will bring great economic and social benefits to develop our own color sorting apparatus, and use it widely in food processing and other industries.
Based on the sorting processing of the color sorting apparatus, an image data acquisition device is built by the use of PCI data acquisition card and the color line CCD camera. And by operating the device through a control interface programmed by VB, the image data of different rice array are acquired, and the sampled data are stored in text files form, as a data source for the study of color-based sorting algorithm followed by.
By the research and analysis of each frame of the image data, we find that the high frequency noise in the image date has a greater impact on the feature extraction of the bad rice. In order to reduce or eliminate this disturbance, the paper attempts to get smooth signals by wavelet filtering method. And according to the relevant theory of digital image processing feature extraction and actual image data characteristics, the k-means clustering analysis is applied to sorting the image data into three categories: the background category, normal rice category and the bad rice category, to get the purpose of extracting the bad points off the image date.
Different from the principle of existing color sorting algorithms, this algorithm does not rely on any artificial operation or transcendental value, but directly determines which category the bad rice particle should be in, by the clustering algorithm based on the internal features of the data.
Finally, we use 3600 frame image data collected from 20 groups of different rice to test the algorithm. The analysis of entire experimental results shows that the algorithm is practical and effective.
本文根据色选机的色选过程，利用PCI数据采集卡及彩色线阵CCD摄像头，搭建了一个图像数据采集实验装置。通过VB编写的控制界面操作该装置，实现对不同大米线阵图像数据的采集，并将采集的数据以文本文档的形式储存，作为后续大米色选算法研究的数据源。
通过对每一帧图像数据的研究与分析，发现图像数据中的高频噪声对于提取大米异色粒的特征信息产生较大影响，为了减小或消除高频噪声的干扰，本文尝试用小波滤波的处理方法以得到平滑的信号。并根据数字图像处理中特征提取的相关理论，结合实际图像数据特征，本文采用k-均值聚类分析方法将图像数据分为三类：背景类、正常米类和异色米类。实现从图像数据中提取异色点的目的。
与现有色选算法原理不同，该算法不需要人为操作或提供某种先验值，而是由聚类分析算法根据输入数据的内部特征，直接确定异色米所在的类。
最后，本文利用对20组不同大米采集的2000多帧图像数据，进行算法验证，从整个实验的结果和分析可知该算法是实际有效的。
Because of its unique and effective screening method, color sorting apparatus is widely used in sorting and processing industry of rice and other foodstuff. At present, our country lags behind some foreign countries in the technology of color sorting apparatus, most manufacturers have to rely on expensive imports of foreign products. Therefore, for china, a large agricultural country, it will bring great economic and social benefits to develop our own color sorting apparatus, and use it widely in food processing and other industries.
Based on the sorting processing of the color sorting apparatus, an image data acquisition device is built by the use of PCI data acquisition card and the color line CCD camera. And by operating the device through a control interface programmed by VB, the image data of different rice array are acquired, and the sampled data are stored in text files form, as a data source for the study of color-based sorting algorithm followed by.
By the research and analysis of each frame of the image data, we find that the high frequency noise in the image date has a greater impact on the feature extraction of the bad rice. In order to reduce or eliminate this disturbance, the paper attempts to get smooth signals by wavelet filtering method. And according to the relevant theory of digital image processing feature extraction and actual image data characteristics, the k-means clustering analysis is applied to sorting the image data into three categories: the background category, normal rice category and the bad rice category, to get the purpose of extracting the bad points off the image date.
Different from the principle of existing color sorting algorithms, this algorithm does not rely on any artificial operation or transcendental value, but directly determines which category the bad rice particle should be in, by the clustering algorithm based on the internal features of the data.
Finally, we use 3600 frame image data collected from 20 groups of different rice to test the algorithm. The analysis of entire experimental results shows that the algorithm is practical and effective.
引文
[1] 刘丽娜．颗粒物料颜色分选系统设计．哈尔滨理工大学硕士学位论文，2006，3：1-3页
[2] 王忠．我国谷物色选机应用技术现状及发展趋势。农机化研究，2006，4：23-25页
[3] 张源．印度大米加工设备技术水平及发展现状．粮食与饲料工业，2002，(2)：13-14
[4] 藏智娟．Z系列光电色选机的技术特性及应用．粮食与饲料工业，2003，(5)：8-9页
[5] 张裕中．光电色选技术及其应用．粮食与饲料工业，1995，(2)：1-5页
[6] Mustafa Bayram, Mehmet D. Oner. Determination of applicability and effects of colour sorting system in bulgur production line. Journal of Food Engineering, 2006(74): 232-239P
[7] Veerendra Singh, S. Mohan Rao. Application of image process ing and radial basis neural network techniques for ore sorting and ore classification. Minerals Engineering, 2005(18): 1412-1420P
[8] 李亨．颜色技术原理及其应用．北京：科学出版社．1997，6：35-103页
[9] 傅德胜，寿益禾。图形图像处理学．南京：东南大学出版社．2001，9：160-265页
[10] 王庆有．CCD应用技术．天津：天津大学出版社．2000，11：30-71页
[11] 许峰．彩色CCD径赛计时系统研究．哈尔滨工程大学硕士学位论文，2006，1：16-19页
[12] 王庆有，何在新等．CCD应用技术发展动向．光电子技术与信息，1994，(2)：2-6页
[13] 程开富．CCD图像传感器的市场与发展．国外电子元器件，2000，(7)：2-7页
[14] 贾正根．摄像机发展新动态．光电子技术与信息，1996，9(2)：14-17页
[15] 孙延奎．小波分析及应用。北京：机械工业出版社．2005，10：219-244页
[16] 叶永伟，杨庆华，应富强．基于小波和区域统计的纹理图像检索系统．浙江工业大学学报，2003，31(3)：306-309页
[17] 王建宇，张峰，周献中，史迎春，骆文．利用小波变换和K均值聚类实现字幕区域分割，2006，18(10)：1508-1512页
[18] Carmen Capilla. Application of the Haar wavelet transform to detect microseismic signal arrivals. Journal of Applied Geophysics, 2006(59): 36-46P
[19] Silvia Bacchelli, Serena Papi. Image denoising using principal component analysis in the wavelet domain. Journal of Computational and Applied Mathematics, 2006(189): 606-621P
[20] C. Zanchettin, T. B. Ludermir. Wavelet filter for noise reduction and signal compression in an artificial nose. Applied Soft Computing, 2007(7): 246-256P
[21] Li Lia, Liangsheng Qub, Xianghui Liaoa. Haar wavelet for machine fault diagnosis. Mechanical Systems and Signal Processing, 2007 (21): 1773-1786P
[22] Abhisek Ukil, Rastko Z ivanovic. Adjusted Haar wavelet for application in the power systems disturbance analysis. Digital Signal Processing. to be published.
[23] 贵琦，柏森，孙静．一种改进的K均值数字图像水印算法．计算机工程与应用，2006，34：166-168页
[24] 方开泰．实用多元统计分析。上海：华东师范大学出版社．1989，9：215-246页
[25] Qing T. Zeng, Juan Pablo Pratt b, Jane Pak b, Dino Ravnic b, Harold Huss b, Steven J. Mentzer b. Feature-guided clustering of multi-dimensional flow cytometry datasets. Journal of Biomedical Informatics, 2007(40): 325-331P
[26] Ying Li, Anthony Vodacek, Yushan Zhu. An automatic statistical segmentation algorithm for extraction of fire and smoke regions. Remote Sensing of Environment, 2007(108): 171-178P
[27] R. J. Kuo, S. Y. Lin a, C.W. Shih b．Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Expert Systems with Applications, 2007(33): 794-808P
[28] Giansalvatore Mecca, Salvatore Raunich. Alessandro Pappalardo. A new algorithm for clustering search results. Data & Knowledge Engineering, 2007(62): 504-522P
[29] Yuen-Hsien Tseng a, Chi-Jen Lin b, Yu-I Lin. Text mining techniques for patent analysis. Information Processing and Management, 2007(43): 1216-1247P
[30] Dingxi Qiu, Ajit C. Tamhane. A Comparative Study of the K-Means Algorithm and the Normal Mixture Model for Clustering: Univariate Case. Journal of Statistical Planning and Inference. to be published.
[31] Amir Ahmad, Lipika Dey. A k-mean clustering algorithm for mixed numeric and categorical data. Data & Knowledge Engineering. 2007, 25(3): 1-25P
[32] Stephen J. Redmond, Conor Heneghan. A method for initialising the K-means clustering algorithm using kd-trees. Pattern Recognition Letters, 2007, 28: 965-973P
[33] 杨善林，李永森，胡笑旋，潘若愚．K—means算法中的k值优化问题研究．系统工程理论与实践，2006，(2)：97-101页
[34] 孙红岩，孙晓鹏，李华。基于K—means聚类方法的三维点云模型分割．计算机工程与应用，2006(10)：42-45页
[35] 侯艳丽，杨国胜．基于小波变换和kd树聚类的快速纹理分割算法．计算机应用，2005，25(1)：114-116页
[36] V. Ejov, J. A. Filar, S. K. Lucas, P. Zograf. Clustering of spectra and fractals of regular graphs. J. Math. Anal. Appl, 2007(333): 236-246P
[37] Zhang YuJin. Image Engineering(Ⅱ) Image analysis (Second edition). Beijing: rsinghua University Press, 2005: 168-170P
[38] 陈书海，傅录祥．实用数字图像处理．北京：科学出版社．2005，6：234-316页
[39] 郑阿奇．Visual Basic实用教程(第2版)．北京：电子工业出版社．2003，8：20-280页
[40] 曾强聪．Visual Basic程序设计基础教程．北京：清华大学出版社．2004，5：2-165页
[41] 黄国祥．RGB颜色空间及其应用研究．中南大学博士学位论文，2002，9：2-4页
[42] 潘泉，张磊，孟晋丽，张洪才．小波滤波方法及应用．北京：清华大学出版社．2005，9：17-61页
[43] 阳明盛，熊西文，林建华．MATLAB基础及数学软件。大连：大连理工大学出版社．2003，8：1-204页
[44] 王敞，陈增强，袁著祉．基于遗传算法的K均值聚类分析．计算机科学，2003，30(2)：163-164页
[45] 王红睿，赵黎明，裴剑．均衡化的改进K均值聚类法。吉林大学学报(信息科学版)，2006，24(2)：172-176页
[46] 申明金．模湖K均值聚类分析用于染料的分类。安庆师范学院学报(自然科学版)，2005，11(2)：41-43页
[47] O. Kleynen, V. Leemans, M.-F. Destain. Selection of the most efficient wavelength bands for 'Jonagold' apple sorting. Postharvest Biology and Technology, 2003(30): 221-232P
[48] G. Nebe-von-Carona, P. J. Stephensb, C. J. Hewittc, J. R. Powella, R. A. Badleya. Analysis of bacterial function by multi-colour fluorescence flow cytometry and single cell sorting. Journal of Microbiological Methods, 2000(42): 97-114P
[49] C. Cifarellia, L. Nieddua, O. Seref b, P. M. Pardalosb. A kernel k-means procedure for classification. Computers & Operations Research, 2007(34): 3154-3161P
[50] D. Trias a, R. Garcia, J. Costa a, N. Blanco a, J. E. Hurtado.Quality control of CFRP by means of digital image processing and statistical point pattern analysis. Composites Science and Technology, 2007(67):2438 - 2446P
[51] Juan Antonio Cuesta-Albertosa, Ricardo Fraimanb. Impartial trimmed k-means for functional data. Computational Statistics & Data Analysis,2007(51):4864 - 4877P
[52] Yuen-Hsien Tseng a, Chi-Jen Lin b, Yu-I Lin. Text mining techniques for patent analysis. Information Processing and Management, 2007(43):1216 - 1247P
[53] Qinran Chen, Wai-kuen Cham, Kar-kin Lee. Extracting eyebrowcontour and chin contour for face recognition. Pattern Recognition,2007(40):2292 - 2300P
[54] Xu-Lei Yang, Qing Song, Yi-Lei Wu. A robust deterministic annealing algorithm for data clustering. Data & Knowledge Engineering,2007(62):84 - 100P
[55] C. Romero, S. Ventura. Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 2007(33):135 -146P
[56] Seung-Hoon Na, In-Su Kang, Jong-Hyeok Lee. Adaptive document clustering based on query-based similarity. Information Processing and Management, 2007(43) :887 - 901P
[57] Desmond J. Highama, Gabriela Kalnaa, Milla Kibbleb. Spectral clustering and its use in bioinformatics. Journal of Computational and Applied Mathematics, 2007(204):25 - 37P
[58] Juan Xua, Opas Chutatapeb, Eric Sungc, Ce Zhengd, Paul Chew Tec Kuand. Optic disk feature extraction via modified deformable model technique for glaucoma analysis. Pattern Recognition, 2007(40):2063 - 2076P
[59] Ulrich Hillenbrand. Consistent parameter clustering: Definition and analysis. Pattern Recognition Letters. 2007(28): 1112-1122P
[60] Sergio Barreto, Carlos Ferreira, Jose Paixao, Beatriz Sousa Santos. Using clustering analysis in a capacitated location-routing problem. European Journal of Operational Research, 2007(179): 968-977P
[61] C. Zanchettin, T. B. hudermir. Wavelet filter for noise reduction and signal compression in an artificial nose. Applied Soft Computing, 2007(7): 246-256P
[62] Sylvain Durand. M-band filtering and nonredundant directional wavelets. Applied Computational Harmonic Analysis, 2007(22): 124-139P
[63] Pavel Mrazek, Joachim Weickert. From two-dimensional nonlinear diffusion to coupled Haar wavelet shrinkage. Journal of Visual Communication Image Representation, 2006(18): 162-175P
[64] Ward Van Aerschot, Maarten Jansen, Adhemar Bultheel. Adaptive splitting for stabilizing 1-D wavelet decompositions on irregular grids. Signal Processing, 2006(86): 2447-2463P
[65] Carmen Capilla. Application of the Haar wavelet transform to detect microseismic signal arrivals. Journal of Applied Geophysics, 2006(59):36-46P
[66] 刘蕾，江洁，张广军．基于CPLD的线阵CCD的驱动及数据采集．电子测量与仪器学报，2006，20(4)：107-110页
[2] 王忠．我国谷物色选机应用技术现状及发展趋势。农机化研究，2006，4：23-25页
[3] 张源．印度大米加工设备技术水平及发展现状．粮食与饲料工业，2002，(2)：13-14
[4] 藏智娟．Z系列光电色选机的技术特性及应用．粮食与饲料工业，2003，(5)：8-9页
[5] 张裕中．光电色选技术及其应用．粮食与饲料工业，1995，(2)：1-5页
[6] Mustafa Bayram, Mehmet D. Oner. Determination of applicability and effects of colour sorting system in bulgur production line. Journal of Food Engineering, 2006(74): 232-239P
[7] Veerendra Singh, S. Mohan Rao. Application of image process ing and radial basis neural network techniques for ore sorting and ore classification. Minerals Engineering, 2005(18): 1412-1420P
[8] 李亨．颜色技术原理及其应用．北京：科学出版社．1997，6：35-103页
[9] 傅德胜，寿益禾。图形图像处理学．南京：东南大学出版社．2001，9：160-265页
[10] 王庆有．CCD应用技术．天津：天津大学出版社．2000，11：30-71页
[11] 许峰．彩色CCD径赛计时系统研究．哈尔滨工程大学硕士学位论文，2006，1：16-19页
[12] 王庆有，何在新等．CCD应用技术发展动向．光电子技术与信息，1994，(2)：2-6页
[13] 程开富．CCD图像传感器的市场与发展．国外电子元器件，2000，(7)：2-7页
[14] 贾正根．摄像机发展新动态．光电子技术与信息，1996，9(2)：14-17页
[15] 孙延奎．小波分析及应用。北京：机械工业出版社．2005，10：219-244页
[16] 叶永伟，杨庆华，应富强．基于小波和区域统计的纹理图像检索系统．浙江工业大学学报，2003，31(3)：306-309页
[17] 王建宇，张峰，周献中，史迎春，骆文．利用小波变换和K均值聚类实现字幕区域分割，2006，18(10)：1508-1512页
[18] Carmen Capilla. Application of the Haar wavelet transform to detect microseismic signal arrivals. Journal of Applied Geophysics, 2006(59): 36-46P
[19] Silvia Bacchelli, Serena Papi. Image denoising using principal component analysis in the wavelet domain. Journal of Computational and Applied Mathematics, 2006(189): 606-621P
[20] C. Zanchettin, T. B. Ludermir. Wavelet filter for noise reduction and signal compression in an artificial nose. Applied Soft Computing, 2007(7): 246-256P
[21] Li Lia, Liangsheng Qub, Xianghui Liaoa. Haar wavelet for machine fault diagnosis. Mechanical Systems and Signal Processing, 2007 (21): 1773-1786P
[22] Abhisek Ukil, Rastko Z ivanovic. Adjusted Haar wavelet for application in the power systems disturbance analysis. Digital Signal Processing. to be published.
[23] 贵琦，柏森，孙静．一种改进的K均值数字图像水印算法．计算机工程与应用，2006，34：166-168页
[24] 方开泰．实用多元统计分析。上海：华东师范大学出版社．1989，9：215-246页
[25] Qing T. Zeng, Juan Pablo Pratt b, Jane Pak b, Dino Ravnic b, Harold Huss b, Steven J. Mentzer b. Feature-guided clustering of multi-dimensional flow cytometry datasets. Journal of Biomedical Informatics, 2007(40): 325-331P
[26] Ying Li, Anthony Vodacek, Yushan Zhu. An automatic statistical segmentation algorithm for extraction of fire and smoke regions. Remote Sensing of Environment, 2007(108): 171-178P
[27] R. J. Kuo, S. Y. Lin a, C.W. Shih b．Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Expert Systems with Applications, 2007(33): 794-808P
[28] Giansalvatore Mecca, Salvatore Raunich. Alessandro Pappalardo. A new algorithm for clustering search results. Data & Knowledge Engineering, 2007(62): 504-522P
[29] Yuen-Hsien Tseng a, Chi-Jen Lin b, Yu-I Lin. Text mining techniques for patent analysis. Information Processing and Management, 2007(43): 1216-1247P
[30] Dingxi Qiu, Ajit C. Tamhane. A Comparative Study of the K-Means Algorithm and the Normal Mixture Model for Clustering: Univariate Case. Journal of Statistical Planning and Inference. to be published.
[31] Amir Ahmad, Lipika Dey. A k-mean clustering algorithm for mixed numeric and categorical data. Data & Knowledge Engineering. 2007, 25(3): 1-25P
[32] Stephen J. Redmond, Conor Heneghan. A method for initialising the K-means clustering algorithm using kd-trees. Pattern Recognition Letters, 2007, 28: 965-973P
[33] 杨善林，李永森，胡笑旋，潘若愚．K—means算法中的k值优化问题研究．系统工程理论与实践，2006，(2)：97-101页
[34] 孙红岩，孙晓鹏，李华。基于K—means聚类方法的三维点云模型分割．计算机工程与应用，2006(10)：42-45页
[35] 侯艳丽，杨国胜．基于小波变换和kd树聚类的快速纹理分割算法．计算机应用，2005，25(1)：114-116页
[36] V. Ejov, J. A. Filar, S. K. Lucas, P. Zograf. Clustering of spectra and fractals of regular graphs. J. Math. Anal. Appl, 2007(333): 236-246P
[37] Zhang YuJin. Image Engineering(Ⅱ) Image analysis (Second edition). Beijing: rsinghua University Press, 2005: 168-170P
[38] 陈书海，傅录祥．实用数字图像处理．北京：科学出版社．2005，6：234-316页
[39] 郑阿奇．Visual Basic实用教程(第2版)．北京：电子工业出版社．2003，8：20-280页
[40] 曾强聪．Visual Basic程序设计基础教程．北京：清华大学出版社．2004，5：2-165页
[41] 黄国祥．RGB颜色空间及其应用研究．中南大学博士学位论文，2002，9：2-4页
[42] 潘泉，张磊，孟晋丽，张洪才．小波滤波方法及应用．北京：清华大学出版社．2005，9：17-61页
[43] 阳明盛，熊西文，林建华．MATLAB基础及数学软件。大连：大连理工大学出版社．2003，8：1-204页
[44] 王敞，陈增强，袁著祉．基于遗传算法的K均值聚类分析．计算机科学，2003，30(2)：163-164页
[45] 王红睿，赵黎明，裴剑．均衡化的改进K均值聚类法。吉林大学学报(信息科学版)，2006，24(2)：172-176页
[46] 申明金．模湖K均值聚类分析用于染料的分类。安庆师范学院学报(自然科学版)，2005，11(2)：41-43页
[47] O. Kleynen, V. Leemans, M.-F. Destain. Selection of the most efficient wavelength bands for 'Jonagold' apple sorting. Postharvest Biology and Technology, 2003(30): 221-232P
[48] G. Nebe-von-Carona, P. J. Stephensb, C. J. Hewittc, J. R. Powella, R. A. Badleya. Analysis of bacterial function by multi-colour fluorescence flow cytometry and single cell sorting. Journal of Microbiological Methods, 2000(42): 97-114P
[49] C. Cifarellia, L. Nieddua, O. Seref b, P. M. Pardalosb. A kernel k-means procedure for classification. Computers & Operations Research, 2007(34): 3154-3161P
[50] D. Trias a, R. Garcia, J. Costa a, N. Blanco a, J. E. Hurtado.Quality control of CFRP by means of digital image processing and statistical point pattern analysis. Composites Science and Technology, 2007(67):2438 - 2446P
[51] Juan Antonio Cuesta-Albertosa, Ricardo Fraimanb. Impartial trimmed k-means for functional data. Computational Statistics & Data Analysis,2007(51):4864 - 4877P
[52] Yuen-Hsien Tseng a, Chi-Jen Lin b, Yu-I Lin. Text mining techniques for patent analysis. Information Processing and Management, 2007(43):1216 - 1247P
[53] Qinran Chen, Wai-kuen Cham, Kar-kin Lee. Extracting eyebrowcontour and chin contour for face recognition. Pattern Recognition,2007(40):2292 - 2300P
[54] Xu-Lei Yang, Qing Song, Yi-Lei Wu. A robust deterministic annealing algorithm for data clustering. Data & Knowledge Engineering,2007(62):84 - 100P
[55] C. Romero, S. Ventura. Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 2007(33):135 -146P
[56] Seung-Hoon Na, In-Su Kang, Jong-Hyeok Lee. Adaptive document clustering based on query-based similarity. Information Processing and Management, 2007(43) :887 - 901P
[57] Desmond J. Highama, Gabriela Kalnaa, Milla Kibbleb. Spectral clustering and its use in bioinformatics. Journal of Computational and Applied Mathematics, 2007(204):25 - 37P
[58] Juan Xua, Opas Chutatapeb, Eric Sungc, Ce Zhengd, Paul Chew Tec Kuand. Optic disk feature extraction via modified deformable model technique for glaucoma analysis. Pattern Recognition, 2007(40):2063 - 2076P
[59] Ulrich Hillenbrand. Consistent parameter clustering: Definition and analysis. Pattern Recognition Letters. 2007(28): 1112-1122P
[60] Sergio Barreto, Carlos Ferreira, Jose Paixao, Beatriz Sousa Santos. Using clustering analysis in a capacitated location-routing problem. European Journal of Operational Research, 2007(179): 968-977P
[61] C. Zanchettin, T. B. hudermir. Wavelet filter for noise reduction and signal compression in an artificial nose. Applied Soft Computing, 2007(7): 246-256P
[62] Sylvain Durand. M-band filtering and nonredundant directional wavelets. Applied Computational Harmonic Analysis, 2007(22): 124-139P
[63] Pavel Mrazek, Joachim Weickert. From two-dimensional nonlinear diffusion to coupled Haar wavelet shrinkage. Journal of Visual Communication Image Representation, 2006(18): 162-175P
[64] Ward Van Aerschot, Maarten Jansen, Adhemar Bultheel. Adaptive splitting for stabilizing 1-D wavelet decompositions on irregular grids. Signal Processing, 2006(86): 2447-2463P
[65] Carmen Capilla. Application of the Haar wavelet transform to detect microseismic signal arrivals. Journal of Applied Geophysics, 2006(59):36-46P
[66] 刘蕾，江洁，张广军．基于CPLD的线阵CCD的驱动及数据采集．电子测量与仪器学报，2006，20(4)：107-110页