回转窑火焰图像的分割与检索方法研究
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摘要
近年来,随着回转窑自动控制水平、计算机软硬件及数字图像处理技术的不断发展,在回转窑生产中计算机看火正在逐渐取代或辅助人工看火。这样便会产生海量的数字火焰图像数据,如何有效的检索和应用这些图像则成为一个比较重要的研究课题。
基于内容的图像检索是对描述图像的各种底层特征进行检索,通过计算这些特征值相似性,返回近似的结果。目前,该课题已经成为一个研究热点,在医学图像数据检索领域、人脸数据库领域、交通工具数据库领域以及其他众多的图像数据库领域中获得广泛应用并取得了较好效果。在此基础上,本文对回转窑火焰图像进行了深入分析和实验研究,对采集的火焰图像进行预处理,分割图像,提取图像的各种特征,建立了图像数据库,研究了火焰图像的检索和聚类挖掘,主要工作和贡献包括如下几点:
1.介绍了回转窑控制系统中图像处理的技术,对基于内容图像检索关键技术和聚类在基于内容图像检索中地应用进行了较为系统的研究和探讨,并对灰色系统理论进行了综述和讨论。
2.针对遗传算法的早熟现象,提出了一种改进的基于遗传算法和Otsu理论的火焰图像分割算法,通过与常用单阈值、基于遗传算法和Otsu理论的分割算法对比,结果表明该改进算法分割效果最好,能够较好地应用于实践中;对分割后的火焰图像,研究了火焰图像的各种特征提取方法和算法:提取了火焰、物料和纹理特征。在此基础上,设计并实现了火焰图像数据库,利用采集得到的数据完成数据库的建设。
3.针对图像检索过程中相似度计算的不足,将数据挖掘中的灰关联分析引入到基于内容的图像检索中,应用灰关联规则改进相似度量度,并以回转窑火焰图像为对象,提出一种基于灰关联度的火焰图像的检索方法,应用灰关联度作为加权因子计算被检索图像与数据库中图像的相似度,从而得到一系列相近检索结果。最后,本文简要介绍了图像检索系统仿真平台的设计与实现。该平台提供一个图像检索算法的仿真环境。
Recently, with the development of automatic rotary, computer hardware, software, and digital image processing technology, the computer has been replacing the people to watch the fire in the production process of rotary kiln. This would generate a mass of flame digital image data, and it was becoming a much more important issue that how to search and use these images effectively.
The basic idea of the content-based image retrieval was used to query the various characteristics which described the images, and then returned the proximate results after calculating the similarity of the feature values. Now, the issue has being a research focus in image processing, which was widely applied in some fields, such as medical image data retrieval, face database, transport database and so on. In this paper a further analysis on the rotary kiln flame images was presented, and some experiments, which included image preprocessing, image segmenting and feature extraction, had been done. After this, the image database system was established, image retrieval and clustering analysis based on the system was introduced. The main contents and contributions are as follows:
Firstly, the image processing technology of the rotary kiln control system was introduced. The research and discussion of key technology and application about clustering in the content-based image retrieval were presented, and the gray system theory was reviewed and discussed.
Secondly, an improved image segmentation algorithm based on genetic algorithm and Otsu theory was proposed. In contrast with the algorithm of single common threshold, general genetic algorithm or Otsu theory, the result of the improved one showed that the split was more effective, and the algorithm could be used effectively in practice. After the flame image being partitioned, we extracted the characteristics of flame, materials and texture. On the basis of these solutions, the flame image database system was designed and implemented.
Thirdly, the gray association analysis based on data mining, which could solute the problems of similarity measurement, was introduced into the content-based image retrieval (CBIR). By the novel CBIR method, the features of the flame image were analyzed, the similarities between the queried images and the images in database were calculated, which based on the weight factors that derived from the gray association rules, and as a consequence, several adjacent flame images were retrieved.
Finally, the image retrieval system was designed, and the simulation platform was introduced briefly, which provided a simulation environment for image retrieval algorithm.
基于内容的图像检索是对描述图像的各种底层特征进行检索,通过计算这些特征值相似性,返回近似的结果。目前,该课题已经成为一个研究热点,在医学图像数据检索领域、人脸数据库领域、交通工具数据库领域以及其他众多的图像数据库领域中获得广泛应用并取得了较好效果。在此基础上,本文对回转窑火焰图像进行了深入分析和实验研究,对采集的火焰图像进行预处理,分割图像,提取图像的各种特征,建立了图像数据库,研究了火焰图像的检索和聚类挖掘,主要工作和贡献包括如下几点:
1.介绍了回转窑控制系统中图像处理的技术,对基于内容图像检索关键技术和聚类在基于内容图像检索中地应用进行了较为系统的研究和探讨,并对灰色系统理论进行了综述和讨论。
2.针对遗传算法的早熟现象,提出了一种改进的基于遗传算法和Otsu理论的火焰图像分割算法,通过与常用单阈值、基于遗传算法和Otsu理论的分割算法对比,结果表明该改进算法分割效果最好,能够较好地应用于实践中;对分割后的火焰图像,研究了火焰图像的各种特征提取方法和算法:提取了火焰、物料和纹理特征。在此基础上,设计并实现了火焰图像数据库,利用采集得到的数据完成数据库的建设。
3.针对图像检索过程中相似度计算的不足,将数据挖掘中的灰关联分析引入到基于内容的图像检索中,应用灰关联规则改进相似度量度,并以回转窑火焰图像为对象,提出一种基于灰关联度的火焰图像的检索方法,应用灰关联度作为加权因子计算被检索图像与数据库中图像的相似度,从而得到一系列相近检索结果。最后,本文简要介绍了图像检索系统仿真平台的设计与实现。该平台提供一个图像检索算法的仿真环境。
Recently, with the development of automatic rotary, computer hardware, software, and digital image processing technology, the computer has been replacing the people to watch the fire in the production process of rotary kiln. This would generate a mass of flame digital image data, and it was becoming a much more important issue that how to search and use these images effectively.
The basic idea of the content-based image retrieval was used to query the various characteristics which described the images, and then returned the proximate results after calculating the similarity of the feature values. Now, the issue has being a research focus in image processing, which was widely applied in some fields, such as medical image data retrieval, face database, transport database and so on. In this paper a further analysis on the rotary kiln flame images was presented, and some experiments, which included image preprocessing, image segmenting and feature extraction, had been done. After this, the image database system was established, image retrieval and clustering analysis based on the system was introduced. The main contents and contributions are as follows:
Firstly, the image processing technology of the rotary kiln control system was introduced. The research and discussion of key technology and application about clustering in the content-based image retrieval were presented, and the gray system theory was reviewed and discussed.
Secondly, an improved image segmentation algorithm based on genetic algorithm and Otsu theory was proposed. In contrast with the algorithm of single common threshold, general genetic algorithm or Otsu theory, the result of the improved one showed that the split was more effective, and the algorithm could be used effectively in practice. After the flame image being partitioned, we extracted the characteristics of flame, materials and texture. On the basis of these solutions, the flame image database system was designed and implemented.
Thirdly, the gray association analysis based on data mining, which could solute the problems of similarity measurement, was introduced into the content-based image retrieval (CBIR). By the novel CBIR method, the features of the flame image were analyzed, the similarities between the queried images and the images in database were calculated, which based on the weight factors that derived from the gray association rules, and as a consequence, several adjacent flame images were retrieved.
Finally, the image retrieval system was designed, and the simulation platform was introduced briefly, which provided a simulation environment for image retrieval algorithm.
引文
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[2]周晓杰,徐德宝,张莉.氧化铝熟料烧结回转窑过程综合自动化系统.吉林大学学报(工学版), 2004, 4(1): 350-353
[3] Zhang J, Tong T S, Li F C, et al. Rotary kiln intelligent control based on flame image processing. In: Proc of IEEE International Conference on Intelligent Processing System. Beijing, China, 1997, 792-796
[4]易正明,刘志明,李元峰.氧化铝回转窑火焰图像处理与量化分析.计算机测量与控制, 2006, 14(2): 186-187
[5]李树涛,王耀南,张昌凡.基于燃烧火焰图像特征的回转窑神经网络控制系统.自动化学报, 2002, 28(4): 591-595
[6]魏兆一.光流算法及其在回转窑窑况识别中的应用研究: [东北大学硕士学位论文].沈阳:东北大学, 2005, 49-53
[7]章兢,张小刚.回转窑集成智能控制系统.电工技术学报, 2002, 17(4): 67-71
[8]王琳灵,潘邵来,吕子剑.红外线扫描在氧化铝熟料窑监控中的应用.抚顺石油学院学报, 2003, 23(4): 35-37
[9]李正熙,张永忠,孙德辉.新型回转窑筒体温度红外扫描计算机监测系统研究.见:首届全国有色金属自动化技术与应用学术年会论文集.北京:万国学术出版社, 2003, 69-70
[10]何昭晖,章兢,张小刚.氧化铝回转窑控制系统的研制.邵阳高等专科学校学报, 2002, 15(1): 37-38
[11]李学军,肖友刚,王梅松.模糊综合评判法在回转窑调窑参数模糊优化中的应用.有色金属, 2002, 55(1): 68-71
[12] Swain M J, Ballard D H. Color indexing. International Journal of Computer Vision, 1991, 7 (1): 11-32
[13] Jain A K, Vailaya A. Image retrieval using color and shape. Pattern Recognition, 1997, 29 (8) :1233-1244
[14] Stricker M A, Orengo M. Similarity of color image. In: Proc of SPIE: Storage and Retrieval for Image and Video DatabasesⅢ. San Jose, USA, 1995, 381-392
[15] Pass G, Zabih R, Miller J. Comparing images using color coherence vectors. In: Proc of ACM Intern Conf Multimedia. Boston, USA, 1996, 324- 427
[16] Huang J, Kumar S R, Zabih R, et al. Image indexing using color correlograms. In: Proc of CPVR’97. London, UK, 1997, 762-768
[17]章毓晋.基于内容的视觉信息检索.北京:科学出版社, 2003, 23-26
[18] Haralick R M, Robert M. Textural feature for image classification. IEEE Trans on System. 1973, 3(6): 610-621
[19] Smith J R, Chang S F. Transform feature for texture classification and discrimination in large image database. In: Proc of IEEE Int'l Conf on Image Processing. Austin, USA, 1994, 34-42
[20] Orchard M T, Bauman C A. Color quantization of image. IEEE Trans Signal Processing. 1991, 39 (12): 2677-2690
[21] Jain A K, Vailaya A. Shape-Based retrieval: A case study with trademark image database. Pattern Recognition, 1998, 31 (9): 1369-1390
[22] Gudivada V N, Raghavan V V. Content-Based image retrieval systems. IEEE Computer, 1995, 28(9): 18-22
[23] Rui Y, Alfred C, Huang T S. Modified fourier descriptor for shape representation-a practical approach. In: Proc of Int'l Workshop on Image Database and Multimedia Search. Amsterdam, Netherlands, 1996, 56-63
[24]汪祖媛,庄镇泉,何劲松.基于形状的小波变换系数广义高斯分布图像检索算法.电子学报, 2003, 31(5): 775-768
[25]唐立军,段立娟,高文.基于内容的图像检索系统.计算机应用, 2000, 10(7): 41-45
[26]李向阳,鲁东明,潘云鹤.基于内容的图像检索技术与系统.计算机研究与发展, 2001, 38 (3) : 342-354
[27] Smith J R, Chang S F. Tools and techniques for color image retrieval. In: Proc of SPIE: Storage and Retrieval for Image and Video Database IV. San Jose, USA, 1996, 426-437
[28] Ambroise C, Seze G, Badran F, et al. Hierarchical clustering of self-organizing maps for cloud classification. Neurocomputing, 2000, 4(6): 47-52
[29] Wang J, Yang W J, Acharya R. Color clustering techniques for color content-based image retrieval from image databases. In: Proc of IEEE Int'l Conference on Multimedia Computing and Systems. Ontario, USA, 1997, 442-449
[30] Renato O, Stehling, Mario A, et al. An adaptive and efficient clustering-based approach for content-based image retrieval in image databases. IEEE Computer, 2001, 12(6): 356-365
[31] Krishnamachari S, Mottaleb M A. Hierarchical clustering algorithm for fast image retrieval. In: Proc of the IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases VII. San Jose, USA, 1999, 96-105
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