摘要
随着信息获取和信息处理技术的快速发展,计算机视觉,即如何利用计算机技术高效准确地从环境图像或者视频中获取相关信息,进而对客观世界中的事物及发生的现象进行分析、判断和决策,已经成为一个非常重要的研究课题。
生物的视觉和智能,目前还是人类认知的处女地。在生物视觉研究中已经发现大量有趣的现象、结构和功能,但是对于总的工作机制和机理还知道的很少。从信息处理的角度,对于已有的关于生物视觉和智能的研究成果进行总结、分析与综合等研究,必将对计算机视觉和人工智能的发展产生强有力的推动作用,同时计算机视觉和人工智能的研究能为生物视觉和生物智能的工作机理提供重要线索。
计算机视觉基于物理计算设备。七十年来,对于计算设备的能力上限,主流的科学家持悲观的态度。究竟什么是计算?计算到底能干什么?这是计算机科学、人工智能、计算机视觉的基本问题,是彻底解决计算机视觉和人工智能的前提条件。自适应性和智能化已经成为困扰当前计算机视觉的最大障碍。如何在复杂的背景下,从样本图像和指定目标中自主获取目标的模式,并利用这些模式对未知环境和图像进行分析和判断;如何利用计算产生自适应性;视觉和智能在信息处理的层面上究竟有什么样的关系等问题,是彻底解决计算机视觉的关键。
本学位论文从生物视觉中总结出能代表视觉信息处理的共同特征的、计算机视觉可利用的生物学证据;在此基础上,提出并论证了演化计算具有比算法更强的表达能力的观点;将演化计算、人工神经网络方法、并行计算等技术融合起来,找到一种适合于目标识别过程中使用的知识表示、知识进化、知识使用的新的计算智能方法;将该方法应用于机器视觉技术研究,解决带钢表面缺陷检测过程中的准确度和实时性问题。本文研究工作主要贡献是:
(1)生物视觉对于高等哺乳动物视觉领域中已经取得的一些相关研究成果进行归纳、总结和假设,从中找出能代表视觉信息处理的生物学证据,包括视觉与智能的同源性、演化机制、并行处理机制、自适应机制、层次处理机制、和竞争机制等等。
(2)演化计算基本理论指出了算法和图灵机的缺陷,对于“图灵机是任意物理计算设备的精确模型”的论断提出了质疑;提出了演化计算的概念和相应的自动机模型,并对它的表达能力进行了论证,证明了演化计算方法比算法或图灵机的表达能力更强。
(3)基于演化计算的模式识别将演化计算用于结构模式识别和统计模式识别,将演化计算方法用于统计模式识别的特征选择、分类器参数,以及结构模式识别中的基元结构、基元关系选择、基元关系提取。该方法不仅使得编程人员的工作强度大大降低,而且得到的参数更为合理。
(4)计算机视觉仿真实验平台设计为了提高视觉系统的开发速度,便于程序的学习、训练、演化,提出将虚拟现实技术引入到计算机视觉系统的设计和知识获取过程之中,设计并建立缺陷检测视觉仿真实验平台,以及目标跟踪视觉仿真实验平台。在仿真实验平台可以进行目标识别、分类训练、缺陷知识获取等研究和开发,具有安全、经济、可重复及不受场地条件限制等优势。
(5)基于演化计算的目标识别采用演化计算的方法获得关于钢坯板表面缺陷目标的显性知识和隐性知识,并利用GPU并行计算对表面缺陷目标进行分割、分类和判断;提出了基于互信息熵的钢坯板速度检测方法。
With the enormous increase in popularity of information acquisition and processing technologies, Computer Vision, which effectively analyzing images or videos from our real world, getting information we are interested in, figuring out what is happening and what should to do, has become a very important research topic.
Biological vision and intelligence full of mystery. Research on biological vision has found a large number of interesting phenomena, structures and functions, however, the working mechanism about them be known is very little. Summary, analysis and synthesis of research results is bound to play a strong role in the development of computer science, artificial intelligence, and computer vision. Computer vision is based on physical computing devices. The maximum capacity of computing devices is mistrusted by the most scientists in the last 70 years. What is computation, and what can they do in the end? Theory of computation is the basic problem and precondition on which computer science, artificial intelligence, computer vision is to dependent.
Adaptively or intelligence is the biggest obstacle in current computer vision area. Adaptively or intelligence is the biggest obstacle in current computer vision area.
The answer to those questions, includes how to obtain models autonomously from images of complex environment, how to analyze and make decision under unknown environment by these models, how to get adaptively or intelligence by computation, and what kind of relationship it is between vision and intelligence when both of them act as information processing, is the key to solve the computer vision.
This thesis summarized biological available evidence about vision and information processing from biological vision. Based on that, proposed and demonstrated evolutionary computation (EC) is more powerful methods than algorithm. A hybrid of EC and ANNs, in which knowledge representation, knowledge evolution, and knowledge utilization based on EC, are proposed. The method of computer vision based on EC is used in Strip surface defect detection. In summary, the works in this thesis can be categorized into the following four aspects.
(1) Biological vision Introduce and summarize the related research about biological vision of mammals, find out biological evidence about information processing, including homology of vision and intelligence, evolution mechanisms, parallel mechanisms, adaptive mechanism, hierarchical processing mechanisms, competition mechanism and so on.
(2) Theory of computation The defects of algorithm or Turing machine are proposed and proved that, Turing machine is not an accurate model of all physical computing devices. Put forward the concept of EC and its automaton models, which are more powerful than algorithm or Turing machine.
(3) Pattern recognition based on EC Evolutionary computation is used in structural pattern recognition and statistical pattern recognition. Many problems, include pattern features selection, classification parameters selection in statistical pattern recognition, and primitives structure, primitives relationship choice, primitives relationship extraction in structural pattern recognition, are benefit from EC. This method not only allows programmers to greatly reduce the intensity of the work, but also to get more reasonable parameters.
(4) Visual simulation platform To improve the development speed of the vision system and to facilitate the learning, training, and evolution process, a visual simulation platform for defect detection and a visual simulation platform for target tracking, which introduce virtual reality technology to computer vision system design and knowledge acquisition process, is proposed, design and build. Under the simulation platform, classification, training, knowledge acquisition in the research and development are more security, economic, repeatable and independent.
(5) Target recognition based on EC To solve the problem of surface defects detection, EC method are used to obtain the explicit knowledge about defects, and ANN method to obtain the recessive knowledge about defects, and and GPU parallel computing is used in defects segmentation, classification and judgments. Proposed a plate speed detection method based on mutual information entropy.
引文
[1]. Crick, Francis. The Computer, the Eye, the Soul[J]. Saturday Review, 1966:53-55.
[2]. Crick, Thinking about the Brain[J]. Scientific American 1979, 241,3:181-188.
[3]. Jeff Hawkins, Sandra Blakeslee. On Intelligence[M], Times Books, 2005:25-36.
[4].钱学森.关于思维科学[M].上海:上海人民出版社.1986:64-74.
[5].徐光佑.计算机视觉[M].北京:清华大学出版社.1999:1-14.
[6]. Negahdaripour, S. Jain, A.K., Final Report of the NSF Workshop on the Challenges in Computer Vision Research[J]. Future Directions of Research, Lahina, Maui, Hawaii, 1991.
[7]. A.M. Turing, Computing machinery and intelligence[J]. Mind Vol. 59,1950: 433–460.
[8]. John E. Hopcroft, Rajeev Motwani,Jeffrey D. Ullman. Introduction to Automata Theory, Languages and Computation[M], 3ed. Pearson Addison-Wesley. 2007:205-350.
[9]. Peter Wegner. Why Interaction is More Powerful than Algorithms[J], Communications of the ACM 40 (5), 1997.
[10]. Marr, D., Vision[M], W.H. Freeman and Company, 1982.
[11]. I Prigogine,Isabelle Stengers.The end of certainty : time, chaos, and the new laws of nature[M]. New York, Free Press, 1997.
[12]. Roger Penrose, Martin Gardner. The Emperor's New Mind:Concerning Computers, Minds, and the Laws of Physics[M]. Oxford University Press, 2002.
[13]. GB/T 14977-2008.热轧钢板表面质量的一般要求[S].北京:中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. 2008,12.
[14].中国金属学会等编. 2006年~2020年中国钢铁工业科学与技术发展指南[M].北京:冶金工业出版社, 2006.10.
[15]. Alexander I. Galushkin. Neural Networks Theory[M]. Springer Press, 2007.
[16]. Ji Tingwei, Gao Jun; Research on GA-ANN integration and its applications to cold extrusion process design[C]. Materials Science Forum, Advances in Materials Manufacturing Science and Technology II - Selected Papers from the 12th International Manufacturing Conference in China , 2006: 897-900.
[17]. Xiaobo Zou, Jiewen Zhao, Yanxiao Li. Apple color grading based on organization feature parameters[J]. Pattern Recognition Letters, vol28(15), 2007: 2046-2053.
[18].谢方军,唐常杰,元昌安.基于基因表达式的演化硬件进化和优化算法[J].计算机辅助设计与图形学学报2005 Vol(07).
[19]. Herrera F, Lozano M, Verdegay JL. Tackling fuzzy genetic algorithms[C]. Winter G, Periaux J, Galan M, Cuesta P (eds.) Genetic algorithms in engineering and computer science, Wiley, USA, 2005: 167-189.
[20]. Abraham A, Grosan C, Han SY, Gelbukh A. Evolutionary multiobjective optimization approach for evolving ensemble of intelligent paradigms for stock market modeling[C]. In: Alexander Gelbukh et al.(eds.) 4th Mexican international conference on artifficial intelligence, Mexico, Lecture notes in computer science,Springer, Berlin Heidelberg New York, pp 673-681.
[21]. Abraham A, Ramos V, Web Usage Mining Using Artifficial Ant Colony Clustering and Genetic Programming, 2003 IEEE Congress on Evolutionary Computation (CEC2003), Australia, IEEE Press, 2003: 1384-1391.
[22]. Yao X. Evolving artiffcial neural networks[J]. Proc IEEE 87(9),1999:423-1447.
[23]. Cai X, Zhang N, Venayagamoorthy GK, Wunsch II DC (2007) Time series prediction with recurrent neural networks trained by a hybrid PSOEA algorithm[J]. Neurocomputing 70(13–15):2342-2353.
[24]. Castillo PA, Merelo JJ, Arenas MG, Romero G (2007) Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters[J]. Inf Sci 177(14):2884-2905.
[25]. Chu B, Kim D, Hong D, Park J, Chung JT, Chung JH, Kim TH (2008) GA-based fuzzy controller design for tunnel ventilation systems[J], Journal of Automation in Construction, 17(2):130–136.
[26]. Liu TY, Yang Y, Wan H, Zhou Q, Gao B, Zeng HJ, Chen Z, Ma WY. An experimental study on large-scale web categorization[C]. In: Ellis A, Hagino T, eds. Proc. of the 14th Int’l World Wide Web Conf (WWW-05). Chiba: ACM Press, 2005:1106-1107.
[27]. Chakrabarti S, Roy S, Soundalgekar M. Fast and accurate text classification via multiple linear discriminant projections[J]. Int’l Journal on Very Large Data Bases, 2003,12(2):170-185.
[28]. Tan S, Cheng X, Wang B, Xu H, Ghanem MM, Guo Y. Using dragpushing to refine centroid text classifiers[J]. In: Proc. of the ACM SIGIR-05. ACM Press, 2005. 653-654.
[29]. Debole F, Sebastiani F. An analysis of the relative hardness of reuters-21578 subsets[J]. Journal of the American Society for Information Science and Technology, 2004,56(6):584-596.
[30]. Lewis DD, Li F, Rose T, Yang Y. A new benchmark collection for text categorization research[J]. Journal of Machine Learning Research, 2004,5(3):361-397.
[31]. Forman G, Cohen I. Learning from little: Comparison of classifiers given little training[C]. Proc. of the 8th European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD-04). Springer-Verlag, 2004. 161-172.
[32]. Wang xue, Xie zhi-jiang. Research on automatic inspection system for defects on precise optical surface based on machine vision[J]. Journal of Chongqing University: English Edition, 2006,5 (2) :89-93.
[33]. D J. Automated Surface Inspection with Solid State Image Sensors[J]. SPIE, 1978, (148):18-25.
[34]. Orton. Waynel L, Hill W.J, Brook R.A. Automatic Visual Inspection of Mocing Steel Surface[J]. British Journal of NDT,1977,(9):242-258.
[35].相泽均著,周源译.冷轧钢板缺陷检测系统[J].世界钢铁,1994,21(2):66-73.
[36].罗志勇.带钢表面缺陷检测系统的发展[J].钢铁,1996,31:127-131.
[37].罗志勇.新型冷轧带钢表面缺陷在线检测系统[J].华中科技大学学报,1996,24(1) :75-78.
[38].张海安.带钢表面缺陷的实时检测及分割定位研究[R].硕士学位论文.辽宁:东北大学,2006.
[39].徐科.冷轧带钢表面质量自动检测系统的在线应用研究[J].冶金自动化,2003,(1):51-53.
[40].胡亮.带钢表面缺陷计算机视觉在线检测系统的涉及[J].无损检测,2003,25(6) :287-291.
[41].吴平川.带钢表面缺陷CCD检测法的研究[J].黑龙江自动化技术与应用,1999,18(5):39-44.
[42].吴贵芳.冷轧带钢表面质量在线检测关键技术的研究[R].北京科技大学博士后论文.北京:北京科技大学,2006.
[43].贾方庆.基于机器视觉的带钢表面缺陷检测系统研究[R].硕士学位论文,重庆:重庆大学,2007.
[44]. Daniel Ashlock. Evolutionary Computation for Modeling and Optimization[M]. Springer Press, 2005
[45]. Milan Sonka, Vaclav Hlavac, and Roger Boyle, Image processing, analysis, and machine vision,2nd Edition[M], Chapman & Hall Computing, London, 2007.
[46]. Breunig, M. M. Location independent pattern recognition using genetic programming[R]. Genetic Algorithms and Genetic Programming at Stanford 1995. Stanford Bookstore, Stanford University. 1995.
[47]. James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil R. Pal. Fuzzy models and algorithms for pattern recognition and image processing. Springer Press, 1999
[48].程刚.视频环境中运动目标的检测与跟踪问题研究[R].硕士学位论文.西安:西安理工大学, 2005.
[49]. DE Goldberg, JH Holland. Machine Learning, Genetic algorithms and machine learning[M]. Springer Press, 2005
[50]. John R. Koza, Martin A. Keane, Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Springer Press, 2005
[51]. JF Winkeler, BS Manjunath. Genetic Programming for object detection. Genetic Programming 1997: Proceedings of the Second Annual Conference. 1997, CA, USA
[52]. Perez, A.L.F. Bittencourt, G. Roisenberg, M. Embodied Evolution with a New Genetic Programming Variation Algorithm. Autonomic and Autonomous Systems, 2008. ICAS 2008.
[53]. Binder, Marc D., Hirokawa Nobutaka, Windhorst Uwe. Encyclopedia of Neuroscience[M]. Springer Reference, 2009.
[54]. Larry R. Squire, Darwin Berg, Floyd Bloom, Sascha du Lac, Anirvan Ghosh. Neuroscience Textbook Set: Fundamental Neuroscience[M], 3ed. Academic Press. 2008.
[55]. Robert A. Wilson, Frank C. Keil.The MIT Encyclopedia of the Cognitive Sciences.The MIT Press. 1999.
[56].顾宝文,吴德正.视觉通道的理论及其在眼科的应用[J].眼科新进展,2000,20(2):156-157.
[57].王建军.神经科学:探索脑[M].北京:高等教育出版社,2004:303-319
[58]. Ungerleider LG, Haxby JV. 'What' and 'where' in the human brain. Curr Opin Neurobiol. 1994 Apr;4(2):157–165.
[59].李朝义, H. C. Nothdurft.猫视皮层复杂细胞感受野结构的动态特性[J].中国科学B辑, 1986,09.
[60]. Schmidt EM, Bak MJ, Hambrecht FT. Feasibility of a visual prosthesis for the blind based on intracortical microstimulation of the visual cortex, Brain, 1996;119-507.
[61]. Majewska, A.; Newton, J., Sur, M. Remodeling of synaptic structure in sensory cortical areas in vivo[J]. J. Neurosci. 2006; 26: 3021-29.
[62]. Sing-Hang Cheung,Fang Fang,Sheng He,and Gordon E. Legge. Retinotopically Specific Reorganization of Visual Cortex for Tactile Pattern Recognition, Current Biology,19(7), 2009.
[63]. Abrams, D., Marques, J. M., Hogg, M. A. The social psychology of inclusion and exclusion[M]. Philadelphia, PA: Psychology Press. 2003.
[64]. Peter H Lindsay, Donald A Norman. Human information processing: Introduction to Psychology. Academic Press, 2nd edition,1977.
[65]. Luck SJ.Neurophysiology of selective attention. Pashler H.ed.Attention[M].New York:Psychology Press,1998:257-295.
[66]. Hopfinger JB,Buonoeore MH.Mangun GR.The neural activation of top-down attentional control[J].Nature Neruosci,2000, 3:284-29l.
[67]. J. Barwise and J. Etchemendy. Turing's World 3.0 for Mac: An Introduction to Computability Theory. Cambridge University Press, 1993.
[68]. Michael Sipser. Introduction to the Theory of Computation[M], 2ed. Course Technology, 2005.
[69]. John E. Hopcroft, Rajeev Motwani, Jeffrey D.Ullman. Introduction to Automata Theory, Languages, and Computation[M],3ed. Addison Wesley,2006.
[70]. Maribel Fernández Models of Computation, An Introduction to Computability Theory[M], Springer-Verlag, 2009.
[71]. Rebecca Goldstein, Incompleteness: The Proof and Paradox of Kurt Godel[M]. W. W. Norton & Company, New York. 2005.
[72]. H.Rogers, Theory of Recursive Functions and Effective Computability[M], McGraw-Hill, New York,1967.
[73]. F. Hennie,Introduction to Computability[M], Addison-Wesley, Masschusetts,1977.
[74]. Prigogine Ilya, Nicolis G. Self-Organization in Non-Equilibrium Systems. Wiley.1977
[75].钱学森,于景元,戴汝为.一个科学新领域—开放的复杂巨系统及其方法论[J].自然杂志, (1), 1990.
[76]. A.L. Barabasi. Taming complexity[J]. Nature Physics, 1:68–70, 2005.
[77]. H.,Holland.隐秩序:适应性造就复杂性[M].上海:上海科技教育出版社.2011.
[78].罗杰彭罗斯.皇帝的新脑,长沙:湖南科学技术出版社.1998.
[79]. Alan Turing. Intelligent machinery[J], Mechanical intelligence, North-Holland, 1992:107-127.
[80]. D. J.Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks[J]. Nature, 393:440-442, 1998.
[81]. Edgar Morin. The Noise and the Message[M]. New York: Telos Press.1977.
[82]. M. Mitchell Waldrop. Complexity: the emerging science at the edge of order and chaos. Simon & Schuster, New York, 1992.
[83]. S. A. Frank. Foundations of social evolution. Princeton University Press, Princeton, NJ, 1998.
[84]. W. D. Hamilton. The genetical evolution of social behaviour. I. J. Theor. Biol., 7:1-16, 1964.
[85]. J. von Neumann and O. Morgenstern. Theory of Games and Economic Behavior. Princeton University Press, Princeton, NJ, 1944.
[86].薛定谔.生命是什么[M].长沙:湖南科学技术出版社.2003.
[87]. Friedrich Cramer. Chaos and Order: The Complex Structure of Living Systems[M].Wiley-VCH,1988.
[88]. Prigogine, Ilya. End of Certainty. The Free Press. 1997.
[89]. Nicolis, G.; Prigogine, I. Exploring complexity: An introduction[M]. New York, NY: W. H. Freeman.1989.
[90]. Prigogine, I. Time, Dynamics and Chaos: Integrating Poincare's 'Non-Integrable Systems'[J], Center for Studies in Statistical Mechanics and Complex Systems at the University of Texas-Austin, United States Department of Energy-Office of Energy Research, Commission of the European Communities .1990.
[91]. Prigogine, Ilya. Chaotic Dynamics and Transport in Fluids and Plasmas: Research Trends in Physics Series[M]. New York: American Institute of Physics.1993.
[92]. Ronald L. Meyer. Roger Sperry and his Chemoaffinity Hypothesis. Neuropsychologia, 1998,36(10): 957–980.
[93]. C. E. Shannon. A mathematical theory of communication[J]. Bell System Technical Journal, vol. 27, pp. 379–423 and 623–656, 1948.
[94]. L. O. Chua. Nonlinear Circuits[J]. IEEE Transactions on Circuits and Systems. CAS-31(1),1984: 69-87.
[95]. P. R. Hobson, A. N. Lansbury. A simple electronic circuit to demonstrate bifurcation and chaos[J]. Physics Education, 31, 1993: 39-43.
[96]. J. H. Lu, G. R. Chen. Generating Multiscroll Chaotic Attractors: Theories, Methods And Applications[J]. International Journal of Bifurcation and Chaos, 16(4), 2006: 775-858.
[97].张荣,徐振源,杨永清.通过同步实现“有序+有序=混沌”的例子[J].物理学报,Vol 01.2011.
[98].朱海梅,吴永萍.一种高速收敛粒子群优化算法[J],控制与决策, Vol 01,2010.
[99].陈涛.一种新的混沌神经网络组织方式研究,科学技术与工程,vol 01,2010.
[100]. Fabio Dercole,Daniele Loiacono, Sergio Rinaldi[C].Synchronization in Ecological Networks: A Byproduct of Darwinian Evolutionary International Journal of Bifurcation and Chaos, 2007,17(7):2435 - 2446.
[101]. Pier Luca Lanzi, Daniele Loiacono,Matteo Zanini. Evolving Classifier Ensembles with Heterogeneous Predictors[M]. In A Compilation of two exciting workshop years . Current advances and future outlooks.Springer-Verlag.2008.
[102]. Gao F., Tong H. Q., Parameter estimation for chaotic system based on particle swarm optimization[J], Acta Physica Sinica 2006. vol.55(2): 577–82.
[103]. Gao F., Tong H. Q., Particle swarm optimization: An efficient method for tracing periodic orbits and controlling chaos[J], Dynamics of Continuous Discrete & Impulsive Systems B–Applications & Algorithms 13: 780–784 Part 2 Suppl. S, DEC 2006.
[104].边肇祺,张学工等.模式识别[M].北京:清华大学出版社,2000.
[105].西奥多里德斯.模式识别[M].北京:电子工业出版社,2006.
[106]. K. Fukunaga, Introduction to Statistical Pattern Recognition[M], Academic Press, 1972.
[107]. R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis[M], John Wiley & Sons, New York, 1973.
[108]. C.M. Bishop, Pattern Recognition and Machine Learning[M], Springer, 2006.
[109]. K. Fukunaga, Introduction to Statistical Pattern Recognition[M], second edition, Academic Press, 1990.
[110]. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification[M], second edition, John Wiley & Sons, New York,2001.
[111].李杨,曾海泉,刘庆华,胡运发.基于knn的快速web文档分类,小型微型计算机系统.2004.
[112].傅京孙.模式识别及其应用[M],北京:科学出版社,1983.
[113].刘正光,申旭刚,程彦,刘还珠.卫星云图结构基元的提取[M].天津大学学报,35(5). 2002:551-554.
[114].刘钊,康立山,蒋良孝,杨林权.用粒子群优化改进算法求解混合整数非线性规划问题.小型微型计算机系统,vol 26.2005.
[115]. Gertych A.Zhang A.Sayre J Bone age assessment of children using a digital hand atlas [M]. 2007:4-5.
[116].罗四维.视觉信息认知计算理论[M].北京:科学出版社,2010: 6-10.
[117]. Mike Muehleman. Standardizing Defect Detection for the surface Inspection of Large Web Steel[Z]. Illumination technologies, Inc. 2000.
[118]. K. Fukushima and S. Miyake, Neocognition: A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position[J]. Pattern Recognition, vol. 15, pp. 455-469, 1982.
[119]. M. Riesenhuber and T. Poggio, Hierarchical models of object recognition in cortex[J]. Nature Neuroscience, 1999. 2: 1019-1025.
[120]. Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and Tomaso Poggio. Robust Object Recognition with Cortex-Like Mechanisms[J]. IEEE Trans Pattern Anal Mach Intell,2007,29(3): 411-426.
[121].寿天德.视觉信息处理的脑机制,第2版[M].合肥:中国科学技术大学出版社,2010.
[122]. Barlow, H. B. Possible principles underlying the transformation of sensory messages[J]. Sensory Communication. W. Rosenblith. Cambridge, MA, MIT Press.1961.
[123]. Olshausen BA, and Field DJ. Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images[J]. Nature, 1996, 381: 607-609.
[124]. William E. Vinje, Jack L. Gallant. Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision[J]. Science, 2000:col. 287. no. 5456:1272-1276.
[125].李志清,施智平,李志欣,史忠植.基于结构相似度的稀疏编码模型[J].软件学报, 21(10), 2010:2410-2419.
[126]. A.Bosch,X.Mufioz,and R.Marti. Which is the best way to organize/classify images by content?[J]. Image and Vision Computing, 2007, 25(6):778-791.
[127]. Doi E,Lewicki MS.Sparse coding of natural images using an overcomplete set of limited capacity units[J]. Advances in Neural Information Processing Systems .Cambridge:The MIT Press,2005.17:377-384.
[128]. Te-Won Lee, Lewicki, M.S.,Girolami, M.,Sejnowski, T.J. Blind source separation of more sources than mixtures using overcomplete representations[J]. IEEE Signal Processing Letters, 1999,6(4):87-90.
[129]. Ning Fu, Xiyuan Peng. Underdetermined blind source separation based on improved combinatorial algorithm for l1-norm minimization[J]. Journal of electronic measurement and instrument, 2009:7, 1-5.
[130]. Ivica Kopriva, Andrzej Cichocki. Blind decomposition of low-dimensional multi-spectral image by sparse component analysis[J]. Journal of Chemometrics 2009, 23(11):590-597.
[131]. Hyvarinen A, Oja E, Hoyer P, Hurri J, Image feature extraction by sparse coding and independent component analysis[J]. International Conference on Pattern Recognition, 1998, 2:1268-1273.
[132]. E. Oja, A. Hyvarinen, P. Hoyer. Image Feature Extraction and Denoising by Sparse Coding[J].Pattern Analysis & Applications,2(2), 104-110.
[133]. Wright J, Yang A.Y, Ganesh A, Sastry S S, Yi Ma .Robust Face Recognition via Sparse Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210 - 227.
[134].孙俊,王文渊,卓晴.基于稀疏编码的提取人脸整体特征算法[J].清华大学学报(自然科学版). 2002, 42(3) :411-413.
[135]. Hughes J, Graham D, & Rockmore D. Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder[J]. Proceedings of the National Academy of Sciences, 2010, 107 (4):1279-1283.
[136]. Joshua B.Tenenbaum and William T.Freeman.Separating style and content with bilinear models[J].Neural Computation,2000, 12(6):1247-1283.
[137]. E.P.Simoncelli.Vision and the statistics of the visual environment[J].Current Opinion in Neurobiology, 2003, 13(2):144-149.
[138]. Byoung-Ki Jeon, Yun-Beom Jung, Ki-Sang Hong. Image segmentation by unsupervised sparse clustering[J]. Pattern Recognition Letters, Volume 27, Issue 14, 15 October 2006:1650-1664.
[139]. S.Pack, S.F.Chang.A knowledge engineering approach for image classification based on probabilistic reasoning systems[J],IEEE International Conference on Multimedia and Expo,2000, (2):1133-1136.
[140]. Li QY,shi J,shi ZZ.A model of attention-guided visual sparse coding[C].Proc. of the 4th IEEE Int'l Conf. on CognitiVe Informatics. Irvine: IEEE Computer Society, 2005:120-125.
[141]. Xianhua Zeng, Siwei Luo, Qingyong Li. An associative sparse coding neural network and applications[J]. Neurocomputing, V73, Issues 4-6, January 2010:684-689.
[142]. Yuanyuan Zuo, Bo Zhang. Robust Hierarchical Framework for Image Classification via Sparse Representation[J]. Tsinghua Science & Technology, V16, Issue 1, February 2011:13-21.
[143]. Zhaoshui He, Andrzej Cichocki, Yuanqing Li, Shengli Xie, Saeid Sanei. K-hyperline clustering learning for sparse component analysis[J]. Signal Processing, V89, Issue 6, June 2009:1011-1022.
[144].向世明.纹理图像统计及其应用研究[D].中国科学院计算技术研究所博士论文, 2004:31-40.
[145].汪荣贵,张佑生,高隽.基于Bayes网络的航空图象理解模型[J].中国科学技术大学学报, 2004(06):745-756.
[146].邹琪,罗四维,郑宇.利用多尺度分析和编组的基于目标的注意计算模型[J].电子学报2006, 34(03):559-562.
[147].罗红根,朱利民,丁汉.基于主动轮廓模型和水平集方法的图像分割技术[J].中国图象图形学报,2006, (3):301-309.
[148]. L. Zelnik-Manor and P. Perona, Self-Tuning Spectral Clustering[J]. Advances in Neural Information Processing Systems 17, 2005:1601-1608.
[149]. Shi J, Malik J. Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysisand Machine Intelligence, 2000.22(8):888-905.
[150]. M.Gu, H.Zha, C.Ding, X.He, H.Simon. Spectral relaxation models and structure analysis for K-way graph clustering and bi-clustering[J]. Penn.State Univ, Computer Science and Engineering, 2001.
[151]. Xiaodi Hou, Liqing Zhang. Dynamic Visual Attention Searching for coding length increments[J]. The Neural Information Processing Systems,2008:681- 688.
[152]. Joliffe I.Principal Component Analysis[M], Springer-Verlag.1986.
[153]. Martinez A. M., Kak A.C., PCA versus LDA[J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(2), 2001:228-233.
[154]. Turk M., Pentland A., Face Recognition Using Eigenfaces[J].Proe.IEEE Conf.Computer Vision and Pattem Recognition, 1991:586-591.
[155]. H.S. Seung, D.D.Lee. The Manifold Ways of Perception. Science. vol.290(5500), pp.2268-2269, 2000.
[156]. J.B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction[J]. Science, vol. 290, 2000: 2319-2323.
[157]. S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, vol. 290, 2000: 2323-2326.
[158]. M. Belkin, P. Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J]. Neural Computation, Vol. 15, Issue 6, 2003:1373–1396 .
[159]. Si Si, Dacheng Tao, Kwok-Ping Chan. Evolutionary Cross-Domain Discriminative Hessian Eigenmaps. IEEE Transactions on Image Processing, 2010: 1075-1086.
[160]. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 15:1373-1396, 2003.
[161]. M. Brand. Charting a manifold. In Neural Information Processing Systems 15. MIT Press, 2003.
[162]. D. L. Donoho and C. Grimes. Hessian eigenmaps: New locally linear embedding techniques for high-dimensional data. Technical Report 2003-08, Stanford Statistics Department, 2003.
[163]. [5] A. Ihler. Nonlinear manifold learning[M]. MIT 6.454 Summary, 2003.
[164].张林波.并行计算导论[M].北京:清华大学出版社,2006:3-6.
[165].刘久星,孙永强. PRAM,BSP和LogP并行模型之间的关系及其比较[J],小型微型计算机系统,20(11),1999:824-827.
[166]. L.Savioja, V.V.limki, J. O.Smith.Real-time additive synthesis with one million sinusoids using a GPU[C]. the AES 128th International Convention.2010.
[167]. A. Kubias, F. Deinzer, T. Feldmann, D. Paulus, B. Schreiber, Th Brunner.2D/3D image registration on the GPU[J]. Pattern Recognition and Image Analysis, Vol. 18(3). 2008:381-389.
[168]. Gddeke, D.; Strzodka, R.: Cyclic Reduction Tridiagonal Solvers on GPUs Applied to Mixed Precision Multigrid[J], IEEE Transactions on Parallel and Distributed Systems, 22(1), 2011: 22-32.
[169].陈定方,李勋祥.基于分布式虚拟现实技术的汽车驾驶,模拟器的研究[J].系统仿真学报, 17(2). 2005:347-350.
[170].齐越.虚拟演播室:结构现状及关键技术[J]中国图形图像学报,2000 ,5(6) , pp 457-460.
[171].倪明田,吴良芝.计算机图形学[M].北京:北京大学出版社, 1999, pp.66-147.
[172].张冰,陈万米,梁亮等.基于OpenGL的小型组机器人足球仿真平台设计.系统仿真学报. 2008 vol20(3),pp.724-728.