计算机视觉图像语义模型的描述方法研究
|
摘要
计算机视觉是模式识别应用的一个重要方面,计算机视觉的目的是通过电子化地感知和理解图像,复制人类的视觉效果。计算机视觉首选要解决好三维物体所形成的二维图像在计算机中的存储、处理、描述、表达和理解操作等问题,它涉及的知识广、内容多,包括光学、物理学、数学、自动化和计算机等学科。计算机视觉就是在多学科的基础上来综合各种知识与技术方法,形成一个具有视觉功能的信息处理系统,从而建立起计算机对外界世界的感观、认知和行为动作,即计算机视觉。
本文针对计算机视觉的图像语义模型方法进行了研究。通过分析和研究目前国内外计算机视觉的现状、主要问题和未来发展的方向,对于计算机视觉中图像识别知识表达的问题,特别是图像识别中的底层属性与高层信息关联等方面的内容,进行了设计与研究,构建了计算机视觉图像语义模型方法的知识结构,为图像识别建立了知识关联,实现计算机视觉图像识别的知识方法。
针对计算机视觉图像语义的问题,本文提出了基于知识结构与方法的图像语义模型理论和方法。论述了语义名词的内涵和概念表达,阐述了图像高级知识相关属性与语义的关系、连接和范畴,定义了图像语义名词概念,建立了图像语义模型的理论基础。
提出了图像语义的特征空间表示法。它建立了图像属性与信息描述的映射关系,描述了图像底层属性、图像对象关系及对象空间关系和图像高级信息的信息映射过程,从三层结构的映射来形成一个基于知识描述图像内容的特征向量。图像语义反映了图像存在的基本特性,即图像的光表现,图像中对象的关系,即空间关系,图像的高级信息,即图像与社会与人的关系。从高级信息的知识角度来分析图像存在与应用的环境问题,解决了图像应用信息在计算机中描述与表达的相关知识问题,为计算机视觉的图像识别应用,建立了一种新的知识方法,提供了可参考的思维方式和结论。
计算机视觉图像语义模型描述了在设定的场景中,对在计算机视觉中直接成像的目标物体图像的描述,对所获取的图像,选择相应属性和对象关系等信息,利用约束机制,将数据信息映射到特征空间上,从而形成一个对图像的描述模型。为了建立图像属性与高层语义之间的关联,提出了基于SVM的图像语义关联法,它描述了图像底层属性到高级描述之间的关联,实现了图像信息的映射和特征表达,解决了图像属性选择和高级描述之间的关联问题。
在计算机视觉系统图像语义模型应用中,解决了语义概念运用和语义特征值表示、数字化的问题,建立了图像语义的数据模式和图像语义数据库。提供了图
The research on the desceiption of image semantic models in computer vision is a very important work. When the computer assembled with vision function, it can help people in all fields. This vision aims to the solution of storage, process, description, expression and understanding image in 2D to 3D objects. The vision system includes based knowledge on Optics, Physics, Mathematics, Automatics and Computer Science. The research integrates each course to generate a new branch for vision. The computer will get the ability of sense, recognition and decision from the integrated.The paper presents a concept and application in computer vision with high knowledge. It has analysed the existing state of computer vision, causes and the demands of development in the future. Especially in the image processing of computer vision, the paper organized the basic knowledge of methods and principles on image processing. It also gave the experience on description, content, and the detail in computer vision of image processing. It can impress studying people a resume and outline about computer vision. Helping human to learn the knowledge about computer vision.In order to slove the problems of self-learning and understanding, computer vision should be constructed on high knowledge molds. The paper presents a method which using the knowledge of image semantics and theory. The semantic concept was introduced with connotation and evolution. This discuss includes the relationship between high knowledge and semantics, linking, category. The summery is a base to the word "semantics" which will be frequently used in computer vision.Semantics is a high description of image attributes. The description is a hierachical structure that contains three layers. This construction extracts the information from the image attributes, relation of objects in space and high information of image. A feature vector which be used in the recognition of vision can be extracted from the three layers. This vector was used to describe image in high knowledge. The semantics presents relations and structures of image and spectral, object and object, image and sensibility. The relation of objects reflects location and state. But the impression refers the image in the society. This research is to find the reason on image reserve and condition in high knowledge. Seeking a new field to serve the society using the rich image resource. This research is an advanced and necessity for the image application using a new idea and efficient results.
The semantic definition and its concepts have been given in beginning of the paper. The related events with semantics also have been interpreted. Image semantics of computer vision is the means of changing the recognition from known to unknown environment. The description is the object that projects to the computer vision/camera in direction. The data was epurated from selected object or objects in image attributes. There is an eigenvalue that has been extracted by the projection of vector space. This discuss mainly connects with the basic application. The description was formed like the projection from image attributes/bottom to high information. The projection is the solution to how to select the image attributes and construct the semantics. In order to realize this, the SVM has been used to build the relation between the attributes and senior information. The principle and methods of generation also have been constructed followed by these definitions. This is the basic theory system of computer vision.In the paper, it also gives the related solutions that how to use the concepts of semantics and the digited description in the application of computer vision. The eigenvalue was given to represent the semantics to feature vector space. There are necessary to offer the pattern recognition and database. In order to describe the semantics, the symbols strings were used to represent the eigenvalue from semantic models. In the operation, the recognition of models and database have also been defined and settled. This work is useful to solve the presentation of semantics, category of concept, models of concept, process and steps. Data has been managed in database.The paper presents the representation of semantic models and semantics structures to heighten the efficiency and reliability of image semantic application. It also solved the key question of semantics storage in the computer. The presentation showed the related complex and experimental results. The semantic models, structure of semantic models and structure of semantic storage have been constructed in high knowledge. The structure of storage was built in cross link-list. This strategy comes from the application and the concept-self. The description of third sects was presented in eigenvalue and characters. The structure reflects the feature of semantics. The link-list can easy realize the linked operation and semantic operation between different semantics. The cross link-list can present the relation and knowledge expression. This storage structure offers a stage for self-study and reform.The rules of semantic generation were constructed for getting semantics. Based
on the rules, generating a semantic just like to extract the eigenvalue from the feature tree. The semantic tree is called description of model(TDM). The prior of depth and clustering were considered to get the semantics from the TDM has been suggested. The threshold M is also considered to use in the clustering to reduce numbers of element. These made the process of generating semantics clear and sample. This work helps people to analyse and find errors in the semantics. The similar computation has been designed to offer the degree for the object in semantic recognition.The paper presents the knowledge operation of image semantic models to solve the problems which how to show the relation between the semantics. From the construction of semantic knowledge and feature vector, the structure can be defined and to be used in semantic operation. In order to manage and operate the knowledge structure, the semantic models and operations have been classified. The operation BNF structure has also been defined. The related degree calculates the relation among the semantic models.There are two kinds of semantic experiment. This strategy is to divide the solution into two parts. The semantic models were designed to bridge image and high knowledge. The semantic application began with the semantic models. Then the experiment constructed the description in high knowledge based on the semantic models. From the experiment, track of human motion and track of road automobile, the result showed the two kinds would be easy accepted and realized. The results compared with that of general method that did not used the semantic model is wonderful. Based on the above, the high knowledge of semantics was efficient to construct eigenvalue in the emulational experiment. The results present the innovative and advantaged of semantic application.Based on the summary, there are some results and prediction for the semantics study and application in the future.The paper has constructed and added some description for computer vision in knowledge structure. This point is to aim the high knowledge to solve some problems in image retrieval and recognition in the computer vision. This work is significance in theory and application. The study exploits a new description for computer vision.
本文针对计算机视觉的图像语义模型方法进行了研究。通过分析和研究目前国内外计算机视觉的现状、主要问题和未来发展的方向,对于计算机视觉中图像识别知识表达的问题,特别是图像识别中的底层属性与高层信息关联等方面的内容,进行了设计与研究,构建了计算机视觉图像语义模型方法的知识结构,为图像识别建立了知识关联,实现计算机视觉图像识别的知识方法。
针对计算机视觉图像语义的问题,本文提出了基于知识结构与方法的图像语义模型理论和方法。论述了语义名词的内涵和概念表达,阐述了图像高级知识相关属性与语义的关系、连接和范畴,定义了图像语义名词概念,建立了图像语义模型的理论基础。
提出了图像语义的特征空间表示法。它建立了图像属性与信息描述的映射关系,描述了图像底层属性、图像对象关系及对象空间关系和图像高级信息的信息映射过程,从三层结构的映射来形成一个基于知识描述图像内容的特征向量。图像语义反映了图像存在的基本特性,即图像的光表现,图像中对象的关系,即空间关系,图像的高级信息,即图像与社会与人的关系。从高级信息的知识角度来分析图像存在与应用的环境问题,解决了图像应用信息在计算机中描述与表达的相关知识问题,为计算机视觉的图像识别应用,建立了一种新的知识方法,提供了可参考的思维方式和结论。
计算机视觉图像语义模型描述了在设定的场景中,对在计算机视觉中直接成像的目标物体图像的描述,对所获取的图像,选择相应属性和对象关系等信息,利用约束机制,将数据信息映射到特征空间上,从而形成一个对图像的描述模型。为了建立图像属性与高层语义之间的关联,提出了基于SVM的图像语义关联法,它描述了图像底层属性到高级描述之间的关联,实现了图像信息的映射和特征表达,解决了图像属性选择和高级描述之间的关联问题。
在计算机视觉系统图像语义模型应用中,解决了语义概念运用和语义特征值表示、数字化的问题,建立了图像语义的数据模式和图像语义数据库。提供了图
The research on the desceiption of image semantic models in computer vision is a very important work. When the computer assembled with vision function, it can help people in all fields. This vision aims to the solution of storage, process, description, expression and understanding image in 2D to 3D objects. The vision system includes based knowledge on Optics, Physics, Mathematics, Automatics and Computer Science. The research integrates each course to generate a new branch for vision. The computer will get the ability of sense, recognition and decision from the integrated.The paper presents a concept and application in computer vision with high knowledge. It has analysed the existing state of computer vision, causes and the demands of development in the future. Especially in the image processing of computer vision, the paper organized the basic knowledge of methods and principles on image processing. It also gave the experience on description, content, and the detail in computer vision of image processing. It can impress studying people a resume and outline about computer vision. Helping human to learn the knowledge about computer vision.In order to slove the problems of self-learning and understanding, computer vision should be constructed on high knowledge molds. The paper presents a method which using the knowledge of image semantics and theory. The semantic concept was introduced with connotation and evolution. This discuss includes the relationship between high knowledge and semantics, linking, category. The summery is a base to the word "semantics" which will be frequently used in computer vision.Semantics is a high description of image attributes. The description is a hierachical structure that contains three layers. This construction extracts the information from the image attributes, relation of objects in space and high information of image. A feature vector which be used in the recognition of vision can be extracted from the three layers. This vector was used to describe image in high knowledge. The semantics presents relations and structures of image and spectral, object and object, image and sensibility. The relation of objects reflects location and state. But the impression refers the image in the society. This research is to find the reason on image reserve and condition in high knowledge. Seeking a new field to serve the society using the rich image resource. This research is an advanced and necessity for the image application using a new idea and efficient results.
The semantic definition and its concepts have been given in beginning of the paper. The related events with semantics also have been interpreted. Image semantics of computer vision is the means of changing the recognition from known to unknown environment. The description is the object that projects to the computer vision/camera in direction. The data was epurated from selected object or objects in image attributes. There is an eigenvalue that has been extracted by the projection of vector space. This discuss mainly connects with the basic application. The description was formed like the projection from image attributes/bottom to high information. The projection is the solution to how to select the image attributes and construct the semantics. In order to realize this, the SVM has been used to build the relation between the attributes and senior information. The principle and methods of generation also have been constructed followed by these definitions. This is the basic theory system of computer vision.In the paper, it also gives the related solutions that how to use the concepts of semantics and the digited description in the application of computer vision. The eigenvalue was given to represent the semantics to feature vector space. There are necessary to offer the pattern recognition and database. In order to describe the semantics, the symbols strings were used to represent the eigenvalue from semantic models. In the operation, the recognition of models and database have also been defined and settled. This work is useful to solve the presentation of semantics, category of concept, models of concept, process and steps. Data has been managed in database.The paper presents the representation of semantic models and semantics structures to heighten the efficiency and reliability of image semantic application. It also solved the key question of semantics storage in the computer. The presentation showed the related complex and experimental results. The semantic models, structure of semantic models and structure of semantic storage have been constructed in high knowledge. The structure of storage was built in cross link-list. This strategy comes from the application and the concept-self. The description of third sects was presented in eigenvalue and characters. The structure reflects the feature of semantics. The link-list can easy realize the linked operation and semantic operation between different semantics. The cross link-list can present the relation and knowledge expression. This storage structure offers a stage for self-study and reform.The rules of semantic generation were constructed for getting semantics. Based
on the rules, generating a semantic just like to extract the eigenvalue from the feature tree. The semantic tree is called description of model(TDM). The prior of depth and clustering were considered to get the semantics from the TDM has been suggested. The threshold M is also considered to use in the clustering to reduce numbers of element. These made the process of generating semantics clear and sample. This work helps people to analyse and find errors in the semantics. The similar computation has been designed to offer the degree for the object in semantic recognition.The paper presents the knowledge operation of image semantic models to solve the problems which how to show the relation between the semantics. From the construction of semantic knowledge and feature vector, the structure can be defined and to be used in semantic operation. In order to manage and operate the knowledge structure, the semantic models and operations have been classified. The operation BNF structure has also been defined. The related degree calculates the relation among the semantic models.There are two kinds of semantic experiment. This strategy is to divide the solution into two parts. The semantic models were designed to bridge image and high knowledge. The semantic application began with the semantic models. Then the experiment constructed the description in high knowledge based on the semantic models. From the experiment, track of human motion and track of road automobile, the result showed the two kinds would be easy accepted and realized. The results compared with that of general method that did not used the semantic model is wonderful. Based on the above, the high knowledge of semantics was efficient to construct eigenvalue in the emulational experiment. The results present the innovative and advantaged of semantic application.Based on the summary, there are some results and prediction for the semantics study and application in the future.The paper has constructed and added some description for computer vision in knowledge structure. This point is to aim the high knowledge to solve some problems in image retrieval and recognition in the computer vision. This work is significance in theory and application. The study exploits a new description for computer vision.
引文
[1] Coiffet P. and Chirouze M.. An Introduction to Robot Technology[M]. Hermes Publishing, 1983
[2] Hai Zhuge. Semantics, Resource and Grid[J]. Future Generation Computer Systems, 2004, 20(1): 1-5
[3] Jiebo Luo, Andreas E. Savakis and Amit Singhal. A Bayesian network-based framework for semantic image understanding[J]. Pattern Recognition, 2005, 38(6):919-934
[4] John P. Eakins. Towards intelligent image retrieval[J]. Pattern Recognition, 2002, 35(1):3-14
[5] Zheng Chen etc. iFind: A Web Image Search Engine[W]. http://citeseer.nj.nec.com/560607.html(2004.02.19)
[6] 杨育彬,李宁,陈世福等。一种基于Bayesian学习的彩色肺癌图像语义描述模型[J],计算机研究与发展,2002,39(12):1618-1624
[7] James W. Woods, Charles A. Sneiderman, Kamran Hameed, Michael J. Ackerman and Charlie Hatton. Using UMLS metathesaurus concepts to describe medical images: dermatology vocabulary[W]. Computers in Biology and Medicine, In Press, Corrected Proof, Available online 16 November 2004
[8] 曾昕琳。动物机器人[J]。太空探索,2003,(6):46-47
[9] 阮秋琦。数字图像处理学[M]。北京:电子工业出版社,2001.1
[10] Daniela Stan and Ishwar K. Sethi. elD: a system for exploration of image databases[J]. Information Processing & Management, 2003, 39(3):335-361
[11] S. Nissen-Meyer, L. K?nig and M. Reiser. Maintaining database consistency in an integrated, heterogeneous HIS-RIS-PACS environment[J]. International Congress Series, 2003, 1256:855-859
[12] Smith J R, Chang S F. Tools and techniques for color image retrieval[C]. SPIE, 1996,2670
[13] 孙即祥,王晓华等。模式识别中的特征提取与计算机视觉不变量[M]。北京:国防工业出版社,2001.9
[14] Yi Tao and W. I. Grosky. Image indexing and retrieval[J]. Journal of visual languages and computing, 2000, :323-343
[15] S. C. Hui, K. Y.Chan,G. Y. Qian. A fax image analysis and management system[J]. Microcomputer Applications. 1997,16(3): 102-108
[16] Shyi-Chyi Cheng, Tzu-Chuan Chou, Chao-Lung Yang and Hung-Yi Chang. A semantic learning for content-based image retrieval using analytical hierarchy process[J]. Expert Systems with Applications, 2005, 28(3):495-505
[17] Gunsel B, Tekalp A. Shape similarity matching for query-by-example[J]. Pattern Recognition, 1998, 31(7): 931-944
[18] 戴青云,李海鹏。基于纹理和形状特征的外观设计专利图像的检索方法[J]。计算机工程与应用,2002,38(3):27-29
[19] Jianping Fan, Yongchang Ji and Lide Wu. Automatic Moving Object Extraction toward Content-Based Video Representation and Indexing[J]. Journal of Visual Communication and Image Representation, 2003, 12(3): 306-347
[20] Zhang Ping and Chen Lihui. A novel feature extraction method and hybrid tree classification for handwritten numeral recognition[J]. Pattern Recognition Letters, 2002, 23(1-3):45-56
[21] B. G. Prasad, K. K. Biswas and S. K. Gupta. Region-based image retrieval using integrated color, shape, and location index[J]. Computer Vision and Image Understanding, 2004, 94(1-3): 193-233
[22] Ioannis Brilakis and Lucio Soibelman. Content-Based Search Engines for construction image databases[J]. Automation in Construction, 2005, 14(4): 537-550
[23] W. I. Grosky. Managing multimedia information in database systems[J]. Communications of the ACM, 1997, (1997): 73-80
[24] Yi Tao and W. I. Crrosky. Image indexing and retrieval[J]. Journal of visual languages and computing, 2000, (2000):323-343
[25] V. E. Ogle & M. Stonebraker. Chabot: retrieval from a relational database of images[J]. IEEE Computer, 1995, (1995):40-48
[26] J. Huang, S. R. Kumar, etc. Image indexing using color correlograms[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, San Juan, Puerto Rico, 1997.6, pages:762-768
[27] J. R. Smith & S. F. Chang, Integrated spatial and point feature map query[W]. ACM Multimedia Systems Journal: www.engin.umd.umich.edu/～wgrosky/Recent_Publications/99NewZealand.pdf(2004.2)
[28] Sung Yug Choi and Jang Myung Lee. Optimal Moving Windows for Real-Time Road Image[C]. Processing of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 2001
[29] Roland Bunschoten etc. Range Estimation from a Pair of Omnidirectional Image[C]. Processing of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 2001
[30] Christophe Collewet etc. Image-based visual servoing on planar objects of unknown shaps[C]. Processing of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 2001
[31] Nicolas Courty etc. Computer animation: A new application for image-based visual servoing[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5):223-228
[32] B. H. Kim etc. Localization of a Mobile Robot using Images of a Moving Target[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5):253-257
[33] Roland Bunschoten etc. Range Estimation from a Pair of Omnidirectional Images[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5): 1174-1179
[34] Krishnaprasad Thirunarayan, Aaron Berkovich and Dan Z. Sokol. An information extraction approach to reorganizing and summarizing specifications. Information and Software Technology, 2005, 47)4:215-232
[35] Minh anh T. Ho etc. Proposal of an Adaptive Vision-based Attentive Tracker for Human Intended Actions[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5): 1239-11244
[36] 王润生。图像理解[M]。长沙:国防科技大学出版社,1995.1
[37] 陈剑赞,老松扬,吴玲达。基于内容的图像检索的发展最新趋势[J]。计算机工程与应用,2002,38(10):47-49
[38] Zoran Steji?, Yasufumi Takama and Kaoru Hirota. Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback[J]. Signal Processing, 2005, 85(2):297-324
[39] 朱兴全,张宏江,刘文印等。iFind:一个结合语义和视觉特征的图像相关反馈检索系统。计算机学报,2002,25(7):681-688
[40] 黄祥林,沈兰荪。基于内容的图像检索技术研究[J]。电子学报,2002.7,30(7):1065-1071
[41] 李向阳,庄越挺,潘云鹤。基于内容的图像检索技术与系统[J]。计算机研 究与发展,2001.3,38(3):343-354
[42] Ma Weiying etc. Benchmarking of image feature for content based retrieval[C]. The Thirty Second Asilomar Conference on Signals, Systems & Computers. California, USA: 1998.1:253-257
[43] C Faloutsos, R Barber, etc. Efficient and effective querying by image content[J]. J intelligent information systems, 1994, 3(1): 231-262
[44] A Pentland, R Picard, etc. Photobook: Tools for content-Based manipulation of image database[A]. SPIE Storage and Retrieval of Image and Video Database Ⅱ[C]. San Jose CA: 1994:34-37
[45] FANG-JUNG HSU, SUH-YIN LEE and BAO-SHUH LIN. 2D C-Tree Spatial Representation for Iconic Image[J]. Journal of Visual Langtugges & Computing, 1999, 10(2):147-164
[46] Jungert, E.: Extended symbolic projections as a knowledge structure for spatial reasoning[C]. In Fourth BPRA Conf on Pattern Recognition. Cambridge, MA: Springer-Verlag, 1988, 343-351
[47] Lee S Y, Hsu F J. 2D C-String: Anew spatial knowledge representation for image database system[J]. Pattern Recognition. 1990, 25(10): 1077-1087
[48] Lee S Y, Hsu F J. Picture algebra for spatial reasoning of iconic image represented in 2D C-String[J]. Pattern Recognition Letters. 1991, 12(12): 425-435
[49] Allen J F. Maintaining knowledge about temporal interval[J]. Communications ACM, 1983, 26(11): 832-843
[50] Huang P W, Jean Y R. Using 2D C+-Strings as spatial knowledge representation for image database systems[J]. Pattern Recognition, 1994. 27(9): 1249-1257
[51] Anthony J. T. Lee and Han-Pang Chiu. 2D Z-string: A new spatial knowledge representation for image databases[J]. Pattern Recognition Letters, 2003, 24(16): 3015-3026
[52] Nabil M etc. Picture similarity retrieval using, the 2D project interval representation[J]. IEEE Trans on Knowledge and Data Engineering, 1996, 8(4):533-539
[53] Y Theodoridis, M Vazirgiannis, T Sellis. Spatiotemporal indexing for image multimedia applications[A]. Proc intl conf multimedia computing and systems[C]. Hiroshima Japan, 1996, 441-448
[54] 马蒂尼奇.语言哲学[M]。北京:商务印书馆,1998
[55] 1982
[56] 1, 1980
[57] 王惠锋,孙正兴,王箭。语义图像检索研究进展[J]。计算机研究与发展,2002,39(5):513-523
[58] 高永英,章毓晋。基于多级描述模型的渐进式图像内容理解[J]。电子学报,2001,29(10):1376-1380
[59] 袁磊,曹奎,冯玉才等。一种基于LSI的图像语义检索技术[J]。华中科技大学学报,2002,30(2):105-107
[60] 王伟凝,余英林。图像的情感语义研究进展[J]。电路与系统学报,2003,8(5):101-109
[61] H Buxton, A Mukerjee. Conceptualizing images[J]. Image and Vision Computing, 2000,18(2):79
[62] Y Zhuang, S Mehrotra, T S Huang. A multimedia information retrieval model based on semantis and visual content[W]. 1999, http://citeseer.nj.nec.com/zhuang99multimedia.html
[63] C Colonbo, etc. Semantics in visual information retrieval[J]. IEEE Multimedia, 1999,6(3):38-53
[64] M Cavazza, etc. Multimedia semantis features and image content description[M]. In: Proc of the 1998 MultiMedia Modeling. Lausanne, Switzerland: IEEE CS Press,1998,39-46
[65] Hai Zhuge. Retrieve images by understanding semantic links and clustering image fragments[J]. Joumal of Systems and Software, 2004, 73(3):455-466
[66] Jozef Sta?ak. A Contribution to Image Semantic Analysis[W]. 2004-8-10, http://www.inforum.cz/inforum2004/english/prispevek.php?prispevek=12
[67] 蔡自兴编著。机器人原理及其应用[M]。长沙:中南工业大学出版社,1988.8
[68] 蔡自兴,徐光祜。人工智能及其应用[M]。北京:清华大学出版社,2003.9
[69] 庄健,王孙安。视觉伺服移动机器人中的图像处理研究[J]。西安交通大学学报,2002.3,36(3):287-290
[70] 王月兰,曾迎生。信息融合技术在彩色图像分割方法中的应用[J]。计算机学报,2000.7,23(7):763-767
[71] 郑南宁著。计算机视觉与模式识别[M]。北京,国防工业出版社,1998.3
[72] Themes etc. Image retrieval for information systems in storage and retrieval for image and videoDatabase Ⅲ[C]. In: Proc of SPIE 2420. San Jose, CA: IS & T and SPIE, 1995,394-405
[73] Horst Eidenberger and Christian Breiteneder. Semantic feature layers in content_based image retrieval: implementation of human world features[C]. Proceedings of International Conference on Control, Automation, Robotic and Vision, Singapore, Singapore, 2002
[74] Jianping Fan, Yuli Gao, Hangzai Luo and Guangyou Xu. Statistical modeling and conceptualization of natural images[J]. Pattern Recognition, 2005, 38(2005):865-885
[75] Ci' an W. Shaffrey, Nick G. Kingsbury. Unsupervised image segmentation via Markov trees and complex wavelets[W]. IEEE 2002 International Conference on Image Processing. Rochester, New York. 2002.9
[76] 石跃祥,蔡自兴。基于内容的智能图像检索系统中反馈信息Agent的设计[J]。计算机工程与应用,2002;38(20):10-12
[77] Ci' an W. Shaffrey, Ian H. Jermyn Nick G. Kingsbury. Psychovisual evaluation of image segmentation algorithms[C]. Proceedings of ACIVS 2002 (Advanced Concepts for Intelligent Vision Systems), Ghent, Belgium, September 9-11, 2002
[78] Daniela Stan etc. Mapping low-level image features to semantic concepts[W]. 2004-08-14, make. cs. nthu.edu.tw:8000/presentation/ppt/peiyi20010413.ppt
[79] Shih-Fu Chang, Hari Sundaram. Structural and semantic analysis of video[C]. ICME 2000, July 28-Aug 2, New York, New York
[80] Yung-Kuan Chan and Chin-Chen Chang. Spatial Similarity Retrieval in Video Databases[J]. Journal of Visual Communication and Image Representation, 2001, 12(2): 107-122
[81] Voorhees E. Using WordNet to Disambiguate Word Senses for Text Retrieval[C]. Proc. 16th Annual ACM SIGIR Cortference on Research and Development in Information Retrieval, Pittsburgh, 171-180, 1993
[82] Ioannis Andreou and Nikitas M. Sgouros. Computing, explaining and visualizing shape similarity in content-based image retrieval[J]. Information Processing & Management, 2005, 41(5):1121-1139
[83] Chang C. H, Hsu C. C. Customaizable multi-engine search tool with clustering[J]. Computer Network and ISDN System, 1997, 29:8-13
[84] 王勋,刘君强,魏贵义。智能学习中的知识表示和知识聚类[J]。计算机工程与应用,2003.07:75-77
[85] Hong Wu. Improving image retrieval with semantic classfication using relevance feedback[W]. 2004-08-15,
[86] Yaowu Xu, Eli Saber and A. Murat Tekalp. Dynamic learning from multiple examples for semantic object segmentation and search[J]. Computer Vision and Image Understanding, 2004, 95(3): 334-353
[87] Pavel Praks, etc. Latent semantic indexing for image retrieval systems[W]. 2004-08-15, www.siam.org/meetings/la03/proceedings/Dvorsky.pdf
[88] Chaomei Chen, etc. Similarity-based image browsing[W]. 2004-08-15, www.pages.drexel.edu/～cc345/papers/wcc2000-iip-100.pdf
[89] 边肇祺,张学工。模式识别[M]。北京:清华大学出版社,2003:83-117,284-303
[90] A Vailaya, A K Jain, H J Zhang. On Image Classification: City Image vs. Landscapes[J]. Pattern Recognition, 1998, 31 (12): 1921-1936
[91] Hsia T C. A Note on Invariant Moments in Image Processing[J]. IEEE Trans. On SMC, 1981, 11(12): 831-834
[92] Qasim Iqbal and J. K. Aggarwal. Using Structure in Content-based Image Retrieval[C]. Proceedings of IASTED Intemational Conference Signal and Image Processing(SIP), Nassau, Bahamas, 1999.10, 129-133
[93] Amon Amir, Sankar Basu, Giridharan Iyengar and etc. A multi-modal system for the retrieval of semantic video events[J]. Computer Vision and Image Understanding, 2004, 96(2): 216-236
[94] Antonio Turiel, Jose M. Delgado and Nestor Parga. Learning efficient internal representations from natural image collections[J]. Neurocomputing, 2004, 58-60: 915-921
[95] Ishikawa H, Suxuki F etc. The model, language, and implementation of an object-oriented multimedia knowledge base management system[J]. ACM Transactions on Database System, 1993, 18(1): 1-50
[96] Chen P P-S. The entity-relationship model, toward a unified view of data[J]. ACM Transactions on Database System, 1976,1 (1):9-36
[97] 陈其明,广义数据模型[博士论文]。北京:清华大学,1988
[98] Liang Wang, Weiming Hu and Tieniu Tan. Recent developments in human motion analysis[J]. Pattern Recognition, 2003, 36(3): 585-601
[99] Gavila D M. The Visual analysis of human movement: A survey[J]. Computer Vision and Image Understanding, 1999,73(1):82-98
[100] Huazhong Ning, Tieniu Tan, Liang Wang and Weiming Hu. People tracking based on motion model and motion constraints with automatic initialization[J]. Pattern Recognition, 2004.7,37(7): 1423-1440
[101] Jinshi Cui and Zengqi Sun. Model-based visual hand posture tracking for guiding a dexterous robotic hand[J]. Optics Communications, 2004.5, 235(4-6):311-318
[102] Huazhong Ning, Tieniu Tan, Liang Wang and Weiming Hu. Kinematics-based tracking of human walking in monocular video sequences[J]. Image and Vision Computing, 2004, 22(5)429-441
[103] Huazhong Ning, Tieniu Tan, Liang Wang and Weiming Hu. People tracking based on motion model and motion constraints with automatic initialization[J]. Pattern Recognition, 20004.7,37(7): 1423-1440
[104] Yueting Zhuang, etc. Apply semantic template to support Content-base Image Retrieval[C]. Proceeding of IS&T and SPIE storage and Retrieval for Media Database 2000. San Jose, California, USA.23-25 January,2000
[105] Tzu-Chuan Chou and Shyi-Chyi Cheng. Design and implementation of a semantic image classification and retrieval of organizational memory information systems using analytical hierarchy process[W]. Omega, In Press, Corrected Proof, Available online 12 October 2004
[106] R. W. Picard. A society of models for video and image libraries[W]. MIT Media Lab. TR#360, Apr. 1996
[107] Yong Rui, etc. Relevance Feedback: A power tool in interactive content-based image retrieval[J]. IEEE Tran on Circuits and Systems for video technology, Special Issue on Segmentation, Description, and Retrieval of Video Content. Sept, 1998. 8(5):644-655
[108] 杨雨东,徐光祐。基于划分_合并和稳健M_估计的运动图像分层表示方法[J]。计算机学报,1999,22(4):364-368
[109] 庄越挺,刘小明,潘云鹤等。运动图像序列的人体三维运动骨架重建[J]。计算机辅助设计与图形学学报,2000,12(4):243-250
[110] WI Grosky~*, DV Sreenath~(**), and F. Fotouhi~(**). Emergent Semantics and the Multimedia. Semantic Web[W]. lsdis.cs.uga.edu/SemNSF/SIGMOD-Record-Dee02/Gorsky.pdf
[111] David j.Fleet. Design and use of linear models for image motion analysis[J]. International journal of computer vision, 2000, 36(3): 171-193
[112] Aggarwal J K, CaiQ. Human motion analysis: A review[M]. In: Proc of the IEEE Nonrigid and Articulated Motion Workshop 1997, Piscataway, NJ: IEEE Computer Socity 1997, 90-102
[113] 何敬东。人体比例和在体育动作简图中的应用[J]。中国学校体育,2003.05:69-70
[114] 马学仁。藏传佛教艺术中的人体绘画比例法研究[J]。西北民族学院学报,2003.01:120-126
[115] 刘小明,庄越挺,潘云鹤。基于模型的人体运动跟踪[J]。计算机研究与发展,1999,36(10):1268-1273
[116] 吴东晖,叶秀清,顾伟康。基于不确定性知识的实时道路场景理解[J]。中国图象图形学报,2003,7(1):69-74
[117] Jain A K, Flynn P J(Eds). 3D object recognition systems[M]. Amsterdam: Elsevier Science Publishers B. V., 1993.
[118] Arman F, Aggarwal J K. Model-based object recognition in dense range image A review[J]. ACM Computing Surveys, 1993, 25(1):5-43.
[119] ALEIX M. MARTiNEZ and JUAN R. SERRA. A New Approach to Object-Related Image Retrieval[J]. Journal of Visual Languages & Computing, 2000, 11(3):345-363
[120] 李庆,周曼丽,柳健。三维物体识别研究进展[J]。中国图象图形学报,2000,5(12):985-993
[121] Suk M, Bhandarkar S M. Three-dimensional object recognition from rang images[M]. ToKyo: Springer-Verlag, 1992.
[122] 丁晓青。汉字识别研究的回顾[J]。电子学报,2002,30(9):1364-1368
[123] 任金昌,赵荣椿,张炜。一种快速有效的印刷体文字识别算法[J]。中国图象图形学报。2001,6(10):1011-1015
[124] Beymer D, etc. A real-time computer vision system for measuring traffic parameters[C]. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition(CVPR'97), Puerto Rico, IEEE Press, 1997, 495-501
[125] Koller D, etc. Model-based object tracking in monocular image sequences of road traffic scenes[J]. International Journal of Computer Vision, 1993, 10(3):257-281
[126] Carlo Combi, Barbara Oliboni and Rosalba Rossato. Merging multimedia presentations and semistructured temporal data: a graph-based model and its application to clinical information[J]. Artificial Intelligence in Medicine, 2005, 34(2):89-112
[127] Katerina Pastra, Horacio Saggion and Yorick Wilks. Extracting relational facts for indexing and retrieval of crime-scene photographs[J]. Knowledge-Based Systems, 2003, 16(5-6):313-320
[128] Rene Visser, etc. Detecting automobiles and people for semantic video retrieval [W]. 2004-08-20, carol, science.uva.nl/～nicu/publications/ICPR02_visser.pdf
[129] Lou Jian-Guang, Liu Qi-Feng, Tan Tie-Niu etc. 3-D model based visual traffic surveillance[J]. ACTA AUTOMATICA SINICA, 2003, 29(3):434-449
[130] Jianping Fan, Yuli Gao, Hangzai Luo and Guangyou Xu. Statistical modeling and conceptualization of natural images[J]. Pattern Recognition, 2005, 38(6):865-885
[131] 林洪文,涂丹,李国辉。基于统计背景模型的运动目标检测方法[J]。计算机工程,2003,29(2):97-99
[132] 汤思维,陈卫东,曹其新,移动机器人多目标彩色视觉跟踪系统[J]。机器人,2003,25(1):10-14
[133] Turk M A, Morgenthaler D G, etc. VITS-A vision system for autonomous land vehicle navigation[J]. IEEE Trans. On PAMI, 1988,10(3)342-361
[134] 吴东晖,叶秀清,顾伟康。基于不确定性知识的实时道路场景理解[J]。中国图象图形学报,2002,7(1):69-74
[135] Stauffer C, etc. Adaptive background mixture models for real-time tracking[C]. In: International Conference of Computer Vision and Pattern Recognition, Ft. Collins:IEEE Press, 1999,2(2):246-252
[136] Huwer S, etc. Adaptive change detection for real-time surveillance applications[C]. In: Workshop on Visual Surveillance in ECCV2000, Ireland: IEEE Press,2000
[137] Ivnov Y, etc. Fast lighting independent background subtraction[C]. In: Proceedings of the IEEE Workshop on Visual Surveillance, India, Bombay:IEEE Press,1998,49-55
[138] Eveland, etc. Background modeling for segmentation of video-rate stereo sequences[C]. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbora:IEEE Press, 1998,266-271
[139] Gordon G, etc. Background estimation and removal based on range and color[C]. International Conference of Computer Vision and Pattern Recognition, Ft. Collins:IEEE Press,1999
[140] J.-R.Ohm, etc.A multi-feature description scheme for image and video database retrieval[W]. 2004-09-01, www.ient.rwth-aachen.de/team/ohm/publi/proce35.pdf
[141] Zeng Zhihong. Lane detection and car tracking on the highway[J]. ACTA AUTOMATICA SINICA,2003, 29(3):450-456
[142] I. Burak ?zer, Wayne Wolf. A graph-based object description for information retrieval in digital image and video libraries[J]. Journal of Visual Communication and Image Representation. 2002, 13(4):425-459
[143] 魏芳,董再励,孙茂相等。用于移动机器人的视觉全局定位系统研究[J]。机器人,2001,23(5):400-404
[144] 丁晓青。汉字识别研究的回顾[J]。电子学报,2002,30(9):1364-1368
[145] 万华林,Morshed U.Chowdhury。基于支持向量机的图像语义分类[J]。软件学报,2003,14(11):1891-1899
[146] 付岩,王耀威,王伟强等。SVM用于基于内容的自然图像分类和检索[J]。计算机学报,2003,26(10):1261-1265
[147] 赵红蕊,阎广建,邓小炼等。一种简单加入空间关系的实用图像分类方法[J]。遥感学报,2003,7(5):358-363
[148] Hu MK.Visual Pattern Recognition by Moment Invariant[J]. IRE Trans information Theory, 1962,1(8): 179-187
[149] Wong RY. Scene matching with invariant moments[J]. Computer Graphics and Image Processing. 1978,8(1): 16-24
[150] Dudani SA, etc. Aircrafe Identification by Moment Invariants[J]. IEEE Trans.,1977,c-26(1):39-45
[151] Pasaltis. Image Normalization by Complex Moments[J]. IEEE Trans. PAMI, 1985,7(1):46-55
[152] 石美红,张军英,李永刚。基于差别特征的纹理图像识别研究[J]。计算机应用,2004,24(1):66-69
[153] 沈建平,沈介文,朱宏明等。聚类方法在图像识别中的应用[J]。计算机应用,2003,23(10):51-52
[154] 王有伟,王渊锋,徐公权。图像定位算法的设计与实现[J]。计算机研究与发展,2002,39(6):732-736
[155] 倪梁康。图像意识的现象学[W]。http://www.xianxiang.com/02050713.htm.2004-11-09
[156] Y Rui, S H Thomas, S F Chang. Image retrieval: Past, present and future[J]. Journal of Visual Communication and Image Representation, 1999, 10(1): 39-62
[157] 蔡自兴,贺汉根,陈虹。未知环境中移动机器人导航控制研究的若干问题 [J]。控制与决策,2002,17(4):385-464
[158] Salichs M A. Moreno L. Navigation of mobile robots: Open questions[J]. Robotica, 2000, 18(3):227-234
[2] Hai Zhuge. Semantics, Resource and Grid[J]. Future Generation Computer Systems, 2004, 20(1): 1-5
[3] Jiebo Luo, Andreas E. Savakis and Amit Singhal. A Bayesian network-based framework for semantic image understanding[J]. Pattern Recognition, 2005, 38(6):919-934
[4] John P. Eakins. Towards intelligent image retrieval[J]. Pattern Recognition, 2002, 35(1):3-14
[5] Zheng Chen etc. iFind: A Web Image Search Engine[W]. http://citeseer.nj.nec.com/560607.html(2004.02.19)
[6] 杨育彬,李宁,陈世福等。一种基于Bayesian学习的彩色肺癌图像语义描述模型[J],计算机研究与发展,2002,39(12):1618-1624
[7] James W. Woods, Charles A. Sneiderman, Kamran Hameed, Michael J. Ackerman and Charlie Hatton. Using UMLS metathesaurus concepts to describe medical images: dermatology vocabulary[W]. Computers in Biology and Medicine, In Press, Corrected Proof, Available online 16 November 2004
[8] 曾昕琳。动物机器人[J]。太空探索,2003,(6):46-47
[9] 阮秋琦。数字图像处理学[M]。北京:电子工业出版社,2001.1
[10] Daniela Stan and Ishwar K. Sethi. elD: a system for exploration of image databases[J]. Information Processing & Management, 2003, 39(3):335-361
[11] S. Nissen-Meyer, L. K?nig and M. Reiser. Maintaining database consistency in an integrated, heterogeneous HIS-RIS-PACS environment[J]. International Congress Series, 2003, 1256:855-859
[12] Smith J R, Chang S F. Tools and techniques for color image retrieval[C]. SPIE, 1996,2670
[13] 孙即祥,王晓华等。模式识别中的特征提取与计算机视觉不变量[M]。北京:国防工业出版社,2001.9
[14] Yi Tao and W. I. Grosky. Image indexing and retrieval[J]. Journal of visual languages and computing, 2000, :323-343
[15] S. C. Hui, K. Y.Chan,G. Y. Qian. A fax image analysis and management system[J]. Microcomputer Applications. 1997,16(3): 102-108
[16] Shyi-Chyi Cheng, Tzu-Chuan Chou, Chao-Lung Yang and Hung-Yi Chang. A semantic learning for content-based image retrieval using analytical hierarchy process[J]. Expert Systems with Applications, 2005, 28(3):495-505
[17] Gunsel B, Tekalp A. Shape similarity matching for query-by-example[J]. Pattern Recognition, 1998, 31(7): 931-944
[18] 戴青云,李海鹏。基于纹理和形状特征的外观设计专利图像的检索方法[J]。计算机工程与应用,2002,38(3):27-29
[19] Jianping Fan, Yongchang Ji and Lide Wu. Automatic Moving Object Extraction toward Content-Based Video Representation and Indexing[J]. Journal of Visual Communication and Image Representation, 2003, 12(3): 306-347
[20] Zhang Ping and Chen Lihui. A novel feature extraction method and hybrid tree classification for handwritten numeral recognition[J]. Pattern Recognition Letters, 2002, 23(1-3):45-56
[21] B. G. Prasad, K. K. Biswas and S. K. Gupta. Region-based image retrieval using integrated color, shape, and location index[J]. Computer Vision and Image Understanding, 2004, 94(1-3): 193-233
[22] Ioannis Brilakis and Lucio Soibelman. Content-Based Search Engines for construction image databases[J]. Automation in Construction, 2005, 14(4): 537-550
[23] W. I. Grosky. Managing multimedia information in database systems[J]. Communications of the ACM, 1997, (1997): 73-80
[24] Yi Tao and W. I. Crrosky. Image indexing and retrieval[J]. Journal of visual languages and computing, 2000, (2000):323-343
[25] V. E. Ogle & M. Stonebraker. Chabot: retrieval from a relational database of images[J]. IEEE Computer, 1995, (1995):40-48
[26] J. Huang, S. R. Kumar, etc. Image indexing using color correlograms[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, San Juan, Puerto Rico, 1997.6, pages:762-768
[27] J. R. Smith & S. F. Chang, Integrated spatial and point feature map query[W]. ACM Multimedia Systems Journal: www.engin.umd.umich.edu/～wgrosky/Recent_Publications/99NewZealand.pdf(2004.2)
[28] Sung Yug Choi and Jang Myung Lee. Optimal Moving Windows for Real-Time Road Image[C]. Processing of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 2001
[29] Roland Bunschoten etc. Range Estimation from a Pair of Omnidirectional Image[C]. Processing of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 2001
[30] Christophe Collewet etc. Image-based visual servoing on planar objects of unknown shaps[C]. Processing of the 2001 IEEE International Conference on Robotics & Automation, Seoul, Korea, May 2001
[31] Nicolas Courty etc. Computer animation: A new application for image-based visual servoing[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5):223-228
[32] B. H. Kim etc. Localization of a Mobile Robot using Images of a Moving Target[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5):253-257
[33] Roland Bunschoten etc. Range Estimation from a Pair of Omnidirectional Images[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5): 1174-1179
[34] Krishnaprasad Thirunarayan, Aaron Berkovich and Dan Z. Sokol. An information extraction approach to reorganizing and summarizing specifications. Information and Software Technology, 2005, 47)4:215-232
[35] Minh anh T. Ho etc. Proposal of an Adaptive Vision-based Attentive Tracker for Human Intended Actions[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul, Korea(2001.5): 1239-11244
[36] 王润生。图像理解[M]。长沙:国防科技大学出版社,1995.1
[37] 陈剑赞,老松扬,吴玲达。基于内容的图像检索的发展最新趋势[J]。计算机工程与应用,2002,38(10):47-49
[38] Zoran Steji?, Yasufumi Takama and Kaoru Hirota. Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback[J]. Signal Processing, 2005, 85(2):297-324
[39] 朱兴全,张宏江,刘文印等。iFind:一个结合语义和视觉特征的图像相关反馈检索系统。计算机学报,2002,25(7):681-688
[40] 黄祥林,沈兰荪。基于内容的图像检索技术研究[J]。电子学报,2002.7,30(7):1065-1071
[41] 李向阳,庄越挺,潘云鹤。基于内容的图像检索技术与系统[J]。计算机研 究与发展,2001.3,38(3):343-354
[42] Ma Weiying etc. Benchmarking of image feature for content based retrieval[C]. The Thirty Second Asilomar Conference on Signals, Systems & Computers. California, USA: 1998.1:253-257
[43] C Faloutsos, R Barber, etc. Efficient and effective querying by image content[J]. J intelligent information systems, 1994, 3(1): 231-262
[44] A Pentland, R Picard, etc. Photobook: Tools for content-Based manipulation of image database[A]. SPIE Storage and Retrieval of Image and Video Database Ⅱ[C]. San Jose CA: 1994:34-37
[45] FANG-JUNG HSU, SUH-YIN LEE and BAO-SHUH LIN. 2D C-Tree Spatial Representation for Iconic Image[J]. Journal of Visual Langtugges & Computing, 1999, 10(2):147-164
[46] Jungert, E.: Extended symbolic projections as a knowledge structure for spatial reasoning[C]. In Fourth BPRA Conf on Pattern Recognition. Cambridge, MA: Springer-Verlag, 1988, 343-351
[47] Lee S Y, Hsu F J. 2D C-String: Anew spatial knowledge representation for image database system[J]. Pattern Recognition. 1990, 25(10): 1077-1087
[48] Lee S Y, Hsu F J. Picture algebra for spatial reasoning of iconic image represented in 2D C-String[J]. Pattern Recognition Letters. 1991, 12(12): 425-435
[49] Allen J F. Maintaining knowledge about temporal interval[J]. Communications ACM, 1983, 26(11): 832-843
[50] Huang P W, Jean Y R. Using 2D C+-Strings as spatial knowledge representation for image database systems[J]. Pattern Recognition, 1994. 27(9): 1249-1257
[51] Anthony J. T. Lee and Han-Pang Chiu. 2D Z-string: A new spatial knowledge representation for image databases[J]. Pattern Recognition Letters, 2003, 24(16): 3015-3026
[52] Nabil M etc. Picture similarity retrieval using, the 2D project interval representation[J]. IEEE Trans on Knowledge and Data Engineering, 1996, 8(4):533-539
[53] Y Theodoridis, M Vazirgiannis, T Sellis. Spatiotemporal indexing for image multimedia applications[A]. Proc intl conf multimedia computing and systems[C]. Hiroshima Japan, 1996, 441-448
[54] 马蒂尼奇.语言哲学[M]。北京:商务印书馆,1998
[55] 1982
[56] 1, 1980
[57] 王惠锋,孙正兴,王箭。语义图像检索研究进展[J]。计算机研究与发展,2002,39(5):513-523
[58] 高永英,章毓晋。基于多级描述模型的渐进式图像内容理解[J]。电子学报,2001,29(10):1376-1380
[59] 袁磊,曹奎,冯玉才等。一种基于LSI的图像语义检索技术[J]。华中科技大学学报,2002,30(2):105-107
[60] 王伟凝,余英林。图像的情感语义研究进展[J]。电路与系统学报,2003,8(5):101-109
[61] H Buxton, A Mukerjee. Conceptualizing images[J]. Image and Vision Computing, 2000,18(2):79
[62] Y Zhuang, S Mehrotra, T S Huang. A multimedia information retrieval model based on semantis and visual content[W]. 1999, http://citeseer.nj.nec.com/zhuang99multimedia.html
[63] C Colonbo, etc. Semantics in visual information retrieval[J]. IEEE Multimedia, 1999,6(3):38-53
[64] M Cavazza, etc. Multimedia semantis features and image content description[M]. In: Proc of the 1998 MultiMedia Modeling. Lausanne, Switzerland: IEEE CS Press,1998,39-46
[65] Hai Zhuge. Retrieve images by understanding semantic links and clustering image fragments[J]. Joumal of Systems and Software, 2004, 73(3):455-466
[66] Jozef Sta?ak. A Contribution to Image Semantic Analysis[W]. 2004-8-10, http://www.inforum.cz/inforum2004/english/prispevek.php?prispevek=12
[67] 蔡自兴编著。机器人原理及其应用[M]。长沙:中南工业大学出版社,1988.8
[68] 蔡自兴,徐光祜。人工智能及其应用[M]。北京:清华大学出版社,2003.9
[69] 庄健,王孙安。视觉伺服移动机器人中的图像处理研究[J]。西安交通大学学报,2002.3,36(3):287-290
[70] 王月兰,曾迎生。信息融合技术在彩色图像分割方法中的应用[J]。计算机学报,2000.7,23(7):763-767
[71] 郑南宁著。计算机视觉与模式识别[M]。北京,国防工业出版社,1998.3
[72] Themes etc. Image retrieval for information systems in storage and retrieval for image and videoDatabase Ⅲ[C]. In: Proc of SPIE 2420. San Jose, CA: IS & T and SPIE, 1995,394-405
[73] Horst Eidenberger and Christian Breiteneder. Semantic feature layers in content_based image retrieval: implementation of human world features[C]. Proceedings of International Conference on Control, Automation, Robotic and Vision, Singapore, Singapore, 2002
[74] Jianping Fan, Yuli Gao, Hangzai Luo and Guangyou Xu. Statistical modeling and conceptualization of natural images[J]. Pattern Recognition, 2005, 38(2005):865-885
[75] Ci' an W. Shaffrey, Nick G. Kingsbury. Unsupervised image segmentation via Markov trees and complex wavelets[W]. IEEE 2002 International Conference on Image Processing. Rochester, New York. 2002.9
[76] 石跃祥,蔡自兴。基于内容的智能图像检索系统中反馈信息Agent的设计[J]。计算机工程与应用,2002;38(20):10-12
[77] Ci' an W. Shaffrey, Ian H. Jermyn Nick G. Kingsbury. Psychovisual evaluation of image segmentation algorithms[C]. Proceedings of ACIVS 2002 (Advanced Concepts for Intelligent Vision Systems), Ghent, Belgium, September 9-11, 2002
[78] Daniela Stan etc. Mapping low-level image features to semantic concepts[W]. 2004-08-14, make. cs. nthu.edu.tw:8000/presentation/ppt/peiyi20010413.ppt
[79] Shih-Fu Chang, Hari Sundaram. Structural and semantic analysis of video[C]. ICME 2000, July 28-Aug 2, New York, New York
[80] Yung-Kuan Chan and Chin-Chen Chang. Spatial Similarity Retrieval in Video Databases[J]. Journal of Visual Communication and Image Representation, 2001, 12(2): 107-122
[81] Voorhees E. Using WordNet to Disambiguate Word Senses for Text Retrieval[C]. Proc. 16th Annual ACM SIGIR Cortference on Research and Development in Information Retrieval, Pittsburgh, 171-180, 1993
[82] Ioannis Andreou and Nikitas M. Sgouros. Computing, explaining and visualizing shape similarity in content-based image retrieval[J]. Information Processing & Management, 2005, 41(5):1121-1139
[83] Chang C. H, Hsu C. C. Customaizable multi-engine search tool with clustering[J]. Computer Network and ISDN System, 1997, 29:8-13
[84] 王勋,刘君强,魏贵义。智能学习中的知识表示和知识聚类[J]。计算机工程与应用,2003.07:75-77
[85] Hong Wu. Improving image retrieval with semantic classfication using relevance feedback[W]. 2004-08-15,
[86] Yaowu Xu, Eli Saber and A. Murat Tekalp. Dynamic learning from multiple examples for semantic object segmentation and search[J]. Computer Vision and Image Understanding, 2004, 95(3): 334-353
[87] Pavel Praks, etc. Latent semantic indexing for image retrieval systems[W]. 2004-08-15, www.siam.org/meetings/la03/proceedings/Dvorsky.pdf
[88] Chaomei Chen, etc. Similarity-based image browsing[W]. 2004-08-15, www.pages.drexel.edu/～cc345/papers/wcc2000-iip-100.pdf
[89] 边肇祺,张学工。模式识别[M]。北京:清华大学出版社,2003:83-117,284-303
[90] A Vailaya, A K Jain, H J Zhang. On Image Classification: City Image vs. Landscapes[J]. Pattern Recognition, 1998, 31 (12): 1921-1936
[91] Hsia T C. A Note on Invariant Moments in Image Processing[J]. IEEE Trans. On SMC, 1981, 11(12): 831-834
[92] Qasim Iqbal and J. K. Aggarwal. Using Structure in Content-based Image Retrieval[C]. Proceedings of IASTED Intemational Conference Signal and Image Processing(SIP), Nassau, Bahamas, 1999.10, 129-133
[93] Amon Amir, Sankar Basu, Giridharan Iyengar and etc. A multi-modal system for the retrieval of semantic video events[J]. Computer Vision and Image Understanding, 2004, 96(2): 216-236
[94] Antonio Turiel, Jose M. Delgado and Nestor Parga. Learning efficient internal representations from natural image collections[J]. Neurocomputing, 2004, 58-60: 915-921
[95] Ishikawa H, Suxuki F etc. The model, language, and implementation of an object-oriented multimedia knowledge base management system[J]. ACM Transactions on Database System, 1993, 18(1): 1-50
[96] Chen P P-S. The entity-relationship model, toward a unified view of data[J]. ACM Transactions on Database System, 1976,1 (1):9-36
[97] 陈其明,广义数据模型[博士论文]。北京:清华大学,1988
[98] Liang Wang, Weiming Hu and Tieniu Tan. Recent developments in human motion analysis[J]. Pattern Recognition, 2003, 36(3): 585-601
[99] Gavila D M. The Visual analysis of human movement: A survey[J]. Computer Vision and Image Understanding, 1999,73(1):82-98
[100] Huazhong Ning, Tieniu Tan, Liang Wang and Weiming Hu. People tracking based on motion model and motion constraints with automatic initialization[J]. Pattern Recognition, 2004.7,37(7): 1423-1440
[101] Jinshi Cui and Zengqi Sun. Model-based visual hand posture tracking for guiding a dexterous robotic hand[J]. Optics Communications, 2004.5, 235(4-6):311-318
[102] Huazhong Ning, Tieniu Tan, Liang Wang and Weiming Hu. Kinematics-based tracking of human walking in monocular video sequences[J]. Image and Vision Computing, 2004, 22(5)429-441
[103] Huazhong Ning, Tieniu Tan, Liang Wang and Weiming Hu. People tracking based on motion model and motion constraints with automatic initialization[J]. Pattern Recognition, 20004.7,37(7): 1423-1440
[104] Yueting Zhuang, etc. Apply semantic template to support Content-base Image Retrieval[C]. Proceeding of IS&T and SPIE storage and Retrieval for Media Database 2000. San Jose, California, USA.23-25 January,2000
[105] Tzu-Chuan Chou and Shyi-Chyi Cheng. Design and implementation of a semantic image classification and retrieval of organizational memory information systems using analytical hierarchy process[W]. Omega, In Press, Corrected Proof, Available online 12 October 2004
[106] R. W. Picard. A society of models for video and image libraries[W]. MIT Media Lab. TR#360, Apr. 1996
[107] Yong Rui, etc. Relevance Feedback: A power tool in interactive content-based image retrieval[J]. IEEE Tran on Circuits and Systems for video technology, Special Issue on Segmentation, Description, and Retrieval of Video Content. Sept, 1998. 8(5):644-655
[108] 杨雨东,徐光祐。基于划分_合并和稳健M_估计的运动图像分层表示方法[J]。计算机学报,1999,22(4):364-368
[109] 庄越挺,刘小明,潘云鹤等。运动图像序列的人体三维运动骨架重建[J]。计算机辅助设计与图形学学报,2000,12(4):243-250
[110] WI Grosky~*, DV Sreenath~(**), and F. Fotouhi~(**). Emergent Semantics and the Multimedia. Semantic Web[W]. lsdis.cs.uga.edu/SemNSF/SIGMOD-Record-Dee02/Gorsky.pdf
[111] David j.Fleet. Design and use of linear models for image motion analysis[J]. International journal of computer vision, 2000, 36(3): 171-193
[112] Aggarwal J K, CaiQ. Human motion analysis: A review[M]. In: Proc of the IEEE Nonrigid and Articulated Motion Workshop 1997, Piscataway, NJ: IEEE Computer Socity 1997, 90-102
[113] 何敬东。人体比例和在体育动作简图中的应用[J]。中国学校体育,2003.05:69-70
[114] 马学仁。藏传佛教艺术中的人体绘画比例法研究[J]。西北民族学院学报,2003.01:120-126
[115] 刘小明,庄越挺,潘云鹤。基于模型的人体运动跟踪[J]。计算机研究与发展,1999,36(10):1268-1273
[116] 吴东晖,叶秀清,顾伟康。基于不确定性知识的实时道路场景理解[J]。中国图象图形学报,2003,7(1):69-74
[117] Jain A K, Flynn P J(Eds). 3D object recognition systems[M]. Amsterdam: Elsevier Science Publishers B. V., 1993.
[118] Arman F, Aggarwal J K. Model-based object recognition in dense range image A review[J]. ACM Computing Surveys, 1993, 25(1):5-43.
[119] ALEIX M. MARTiNEZ and JUAN R. SERRA. A New Approach to Object-Related Image Retrieval[J]. Journal of Visual Languages & Computing, 2000, 11(3):345-363
[120] 李庆,周曼丽,柳健。三维物体识别研究进展[J]。中国图象图形学报,2000,5(12):985-993
[121] Suk M, Bhandarkar S M. Three-dimensional object recognition from rang images[M]. ToKyo: Springer-Verlag, 1992.
[122] 丁晓青。汉字识别研究的回顾[J]。电子学报,2002,30(9):1364-1368
[123] 任金昌,赵荣椿,张炜。一种快速有效的印刷体文字识别算法[J]。中国图象图形学报。2001,6(10):1011-1015
[124] Beymer D, etc. A real-time computer vision system for measuring traffic parameters[C]. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition(CVPR'97), Puerto Rico, IEEE Press, 1997, 495-501
[125] Koller D, etc. Model-based object tracking in monocular image sequences of road traffic scenes[J]. International Journal of Computer Vision, 1993, 10(3):257-281
[126] Carlo Combi, Barbara Oliboni and Rosalba Rossato. Merging multimedia presentations and semistructured temporal data: a graph-based model and its application to clinical information[J]. Artificial Intelligence in Medicine, 2005, 34(2):89-112
[127] Katerina Pastra, Horacio Saggion and Yorick Wilks. Extracting relational facts for indexing and retrieval of crime-scene photographs[J]. Knowledge-Based Systems, 2003, 16(5-6):313-320
[128] Rene Visser, etc. Detecting automobiles and people for semantic video retrieval [W]. 2004-08-20, carol, science.uva.nl/～nicu/publications/ICPR02_visser.pdf
[129] Lou Jian-Guang, Liu Qi-Feng, Tan Tie-Niu etc. 3-D model based visual traffic surveillance[J]. ACTA AUTOMATICA SINICA, 2003, 29(3):434-449
[130] Jianping Fan, Yuli Gao, Hangzai Luo and Guangyou Xu. Statistical modeling and conceptualization of natural images[J]. Pattern Recognition, 2005, 38(6):865-885
[131] 林洪文,涂丹,李国辉。基于统计背景模型的运动目标检测方法[J]。计算机工程,2003,29(2):97-99
[132] 汤思维,陈卫东,曹其新,移动机器人多目标彩色视觉跟踪系统[J]。机器人,2003,25(1):10-14
[133] Turk M A, Morgenthaler D G, etc. VITS-A vision system for autonomous land vehicle navigation[J]. IEEE Trans. On PAMI, 1988,10(3)342-361
[134] 吴东晖,叶秀清,顾伟康。基于不确定性知识的实时道路场景理解[J]。中国图象图形学报,2002,7(1):69-74
[135] Stauffer C, etc. Adaptive background mixture models for real-time tracking[C]. In: International Conference of Computer Vision and Pattern Recognition, Ft. Collins:IEEE Press, 1999,2(2):246-252
[136] Huwer S, etc. Adaptive change detection for real-time surveillance applications[C]. In: Workshop on Visual Surveillance in ECCV2000, Ireland: IEEE Press,2000
[137] Ivnov Y, etc. Fast lighting independent background subtraction[C]. In: Proceedings of the IEEE Workshop on Visual Surveillance, India, Bombay:IEEE Press,1998,49-55
[138] Eveland, etc. Background modeling for segmentation of video-rate stereo sequences[C]. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbora:IEEE Press, 1998,266-271
[139] Gordon G, etc. Background estimation and removal based on range and color[C]. International Conference of Computer Vision and Pattern Recognition, Ft. Collins:IEEE Press,1999
[140] J.-R.Ohm, etc.A multi-feature description scheme for image and video database retrieval[W]. 2004-09-01, www.ient.rwth-aachen.de/team/ohm/publi/proce35.pdf
[141] Zeng Zhihong. Lane detection and car tracking on the highway[J]. ACTA AUTOMATICA SINICA,2003, 29(3):450-456
[142] I. Burak ?zer, Wayne Wolf. A graph-based object description for information retrieval in digital image and video libraries[J]. Journal of Visual Communication and Image Representation. 2002, 13(4):425-459
[143] 魏芳,董再励,孙茂相等。用于移动机器人的视觉全局定位系统研究[J]。机器人,2001,23(5):400-404
[144] 丁晓青。汉字识别研究的回顾[J]。电子学报,2002,30(9):1364-1368
[145] 万华林,Morshed U.Chowdhury。基于支持向量机的图像语义分类[J]。软件学报,2003,14(11):1891-1899
[146] 付岩,王耀威,王伟强等。SVM用于基于内容的自然图像分类和检索[J]。计算机学报,2003,26(10):1261-1265
[147] 赵红蕊,阎广建,邓小炼等。一种简单加入空间关系的实用图像分类方法[J]。遥感学报,2003,7(5):358-363
[148] Hu MK.Visual Pattern Recognition by Moment Invariant[J]. IRE Trans information Theory, 1962,1(8): 179-187
[149] Wong RY. Scene matching with invariant moments[J]. Computer Graphics and Image Processing. 1978,8(1): 16-24
[150] Dudani SA, etc. Aircrafe Identification by Moment Invariants[J]. IEEE Trans.,1977,c-26(1):39-45
[151] Pasaltis. Image Normalization by Complex Moments[J]. IEEE Trans. PAMI, 1985,7(1):46-55
[152] 石美红,张军英,李永刚。基于差别特征的纹理图像识别研究[J]。计算机应用,2004,24(1):66-69
[153] 沈建平,沈介文,朱宏明等。聚类方法在图像识别中的应用[J]。计算机应用,2003,23(10):51-52
[154] 王有伟,王渊锋,徐公权。图像定位算法的设计与实现[J]。计算机研究与发展,2002,39(6):732-736
[155] 倪梁康。图像意识的现象学[W]。http://www.xianxiang.com/02050713.htm.2004-11-09
[156] Y Rui, S H Thomas, S F Chang. Image retrieval: Past, present and future[J]. Journal of Visual Communication and Image Representation, 1999, 10(1): 39-62
[157] 蔡自兴,贺汉根,陈虹。未知环境中移动机器人导航控制研究的若干问题 [J]。控制与决策,2002,17(4):385-464
[158] Salichs M A. Moreno L. Navigation of mobile robots: Open questions[J]. Robotica, 2000, 18(3):227-234