多信息融合图像边缘特征提取及图像配准研究与应用
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摘要
多源信息融合技术是协同利用多源信息以获得对同一事物或目标更客观、更本质认识的信息综合处理技术,最早出现于20世纪70年代。从诞生起,多源信息融合技术就吸引了人们越来越多的关注,对整个信息科学产生了极大的影响,图像融合是多源信息融合技术主要应用领域之一。研究表明,在人类接受到的信息中图像等视觉信息所占比重达到75%,图像中蕴涵着丰富的信息,如何获取这些信息并采用有效的手段进行综合利用是图像模式识别领域中的重要课题。
本文利用多源信息融合思想,全文围绕如何融合多图像信息以实现图像边缘特征稳定提取,及利用边缘及其二次特征进行图像配准为主线展开研究。其中图像边缘检测与图像配准是文中两个联系紧密的主要部分。图像边缘是一种重要的视觉信息,边缘检测是计算机视觉后续工作的基础和前提。图像配准属于图像模式识别研究领域,主要用于多图像融合,在遥感和军事领域有着重大的现实意义。
本文对边缘检测、边缘二次特征提取及图像配准做了一定研究,以提高视觉检测、目标识别及图像合成的稳定性和实时性为研究目标。并将研究应用于AGVS系统、SAR图像处理及数字字符识别等领域。本文的主要工作和创新点如下:
(1)提出一种改进鲁棒Fisher降维方法,通过引入权值函数和调整因子降低“外点”对降维方向计算的干扰,对多维特征向量进行降维,克服了应用贝叶斯理论进行统计推理时,特征维数与小训练样本、特征相关性及计算效率的矛盾。根据边缘的本质特点,构造多级尺度上的绝对梯队和相对梯度作为多维图像信息,降维后利用贝叶斯理论加以融合完成边缘检测。并将该算法运用于复杂场景边缘检测及AGVS系统导引线检测中。
(2)对DS证据理论进行研究,提出一种改进相关证据合成方法,利用证据可信度分布计算出证据间相关度,克服了相关证据合成时的超估计及提高合成结果的合理性,并将传统两相关证据合成推广至多相关证据合成。进而建立了一种基于证据理论的非学习直接融合边缘检测模型,将该模型应用于SAR图像边缘检测中,融合了两级尺度上的ROA算子及梯度算子。该方法最大优点是无需学习过程,可对多图像信息进行直接融合实现边缘检测。
(3)提出一种基于边缘拟合的图像配准方法。以边缘为基本特征,通过可变精度拟合算法将边缘拟合成方便表达的直线特征。根据边缘直线与图像配准的关系,对其进行筛选和可信度赋值,并利用加权投票算法实现图像配准,保证配准的稳定性和实时性。
(4)根据图像配准特点,将Hausdorff距离转换成归一化的相似度值。改进边缘曲率计算,更稳定地提取边缘角点。并利用“Local jet”、边缘及距离约束,提高计算效率。利用改进Hausdorff距离和形态学实现快速稳定的图像配准。将上述方法应用于AGVS系统路标识别、SAR图像和航拍图像配准中,结果表明该方法有效可行。
(5)图像匹配是图像配准的一种特殊形式,针对钢坯标号和AGVS系统工位号识别,分别提出两种数字字符识别方法。针对钢坯标号,利用隶属度概念改进传统模板匹配方法,对字符和模板进行隶属度分配,提高识别稳定性。对于AGVS系统工位号,则提出一次倾斜校正和二次自校正,使失真字符接近正常字符,并利用两级模板匹配方法对字符进行快速稳定识别。
Multi-source information fusion is the technique that utilizes different information from the same object synthetically in order to obtain the more objective and entitative information for this object. Multi-source information fusion technique had its beginnings the early 1970's firstly. Ever since its emergence, this technique has made a strong impact on entire information science, such as image fusion. At the same time, studies show that vision information achieves 75% in all the information received by human. Images contain rich information, and how to obtain this information and utilize it synthetically by effective method is important subject in the pattern recognition field.
This dissertation applies the idea of multi-source information fusion. The whole dissertation is closely linked to edge detection based on fusing multiple information, secondary edge feature extraction and register multiple images based on edge feature. The edge detection and image registration are the two main topics in this dissertation. Image edge is the most important information in image, and edge detection is the basis for the follow-up work generally. And image registration belongs to image pattern recognition research field. And the image registration technique is used mainly for multiple images fusion and has practical significance in the field of military and remote sensing.
The work in the dissertation is focus on the studying of edge detection, secondary edge feature extraction and image registration. The main goal is to improve the stability and real-time performance. And this study is applied to AGVS, SAR image and numeral character recognition, etc. The major contributions in the dissertation are as follows:
(1) To overcome the contradiction between multi-dimensional vector and small training set, element correlation in vectors, computational efficiency when applying Bayesian statistical inference theory, an improved dimensional reduction method is proposed for multi-dimensional data space. The reduction direction computation is robust to outliers by introducing weight function and adjustment factor. Absolute gradient and relative gradient at several scales are constructed to be the multi-dimensional image information. Firstly, the multi-dimensional image information is reduced. Then Bayesian statistical inference theory is employed to fuse multiple features to accomplish edge detection. This method is applied to edge detection in complex scene and guided line detection in AGVS.
(2) With the research on DS evidence theory, an improved relevant evidence fusion algorithm is proposed. This method overcomes overestimation with evidence fusion, improve the reasonableness of fusion result and suitable for multiple relevant evidence fusion. Correlation degree is computed by the contribution of evidence reliability. Then a novel edge detection model based on evidence theory is also constructed. This edge detection method is used to fuse ROA operator responses and gradient operator responses at two scales in order to realize SAR image edge detection. The major advantage of this method is fusing information directly to realize edge detection without learning process.
(3) A novel image registration method based on edge fitting is proposed. This method is based on edge feature and fit these edges to straight line feature which can be expressed with mathematical method easily through an alterable precision fitting algorithm. Based the relationship of edge and image registration, straight line is screened and set a reliability value. And a weighted voting algorithm is employed to perform registration process to ensure the registration performance.
(4) According to the character of image registration, traditional Hasudorff distance is transformed to a normalized similarity value.. The traditional edge curvature computation is adjusted in order to extract the edge corners more stable. "Local jet", edge geometry and distance sequence restriction are used to improve the computation efficiency. Improved Hausdorff distance and morphological method are employed to perform registration process. This registration method is applied to route mark recognition in AGVS, SAR and aerial images registration. Experiments show that this registration method is effective and feasible.
(5) Image template matching is the spectral form of image registration. For the two application background of characters on steel billets and working station mark in AGVS, two novel numeral character recognition methods are proposed. For characters on steel billets, classical template matching technique was improved by membership assignment conception in order to improve the recognition stability. For working station mark in AGVS, first skew rectify and second own rectify are proposed to make distorted character closed to natural character. Then the two stage template matching method is employed to recognize the numeral guide character fast and stably.
本文利用多源信息融合思想,全文围绕如何融合多图像信息以实现图像边缘特征稳定提取,及利用边缘及其二次特征进行图像配准为主线展开研究。其中图像边缘检测与图像配准是文中两个联系紧密的主要部分。图像边缘是一种重要的视觉信息,边缘检测是计算机视觉后续工作的基础和前提。图像配准属于图像模式识别研究领域,主要用于多图像融合,在遥感和军事领域有着重大的现实意义。
本文对边缘检测、边缘二次特征提取及图像配准做了一定研究,以提高视觉检测、目标识别及图像合成的稳定性和实时性为研究目标。并将研究应用于AGVS系统、SAR图像处理及数字字符识别等领域。本文的主要工作和创新点如下:
(1)提出一种改进鲁棒Fisher降维方法,通过引入权值函数和调整因子降低“外点”对降维方向计算的干扰,对多维特征向量进行降维,克服了应用贝叶斯理论进行统计推理时,特征维数与小训练样本、特征相关性及计算效率的矛盾。根据边缘的本质特点,构造多级尺度上的绝对梯队和相对梯度作为多维图像信息,降维后利用贝叶斯理论加以融合完成边缘检测。并将该算法运用于复杂场景边缘检测及AGVS系统导引线检测中。
(2)对DS证据理论进行研究,提出一种改进相关证据合成方法,利用证据可信度分布计算出证据间相关度,克服了相关证据合成时的超估计及提高合成结果的合理性,并将传统两相关证据合成推广至多相关证据合成。进而建立了一种基于证据理论的非学习直接融合边缘检测模型,将该模型应用于SAR图像边缘检测中,融合了两级尺度上的ROA算子及梯度算子。该方法最大优点是无需学习过程,可对多图像信息进行直接融合实现边缘检测。
(3)提出一种基于边缘拟合的图像配准方法。以边缘为基本特征,通过可变精度拟合算法将边缘拟合成方便表达的直线特征。根据边缘直线与图像配准的关系,对其进行筛选和可信度赋值,并利用加权投票算法实现图像配准,保证配准的稳定性和实时性。
(4)根据图像配准特点,将Hausdorff距离转换成归一化的相似度值。改进边缘曲率计算,更稳定地提取边缘角点。并利用“Local jet”、边缘及距离约束,提高计算效率。利用改进Hausdorff距离和形态学实现快速稳定的图像配准。将上述方法应用于AGVS系统路标识别、SAR图像和航拍图像配准中,结果表明该方法有效可行。
(5)图像匹配是图像配准的一种特殊形式,针对钢坯标号和AGVS系统工位号识别,分别提出两种数字字符识别方法。针对钢坯标号,利用隶属度概念改进传统模板匹配方法,对字符和模板进行隶属度分配,提高识别稳定性。对于AGVS系统工位号,则提出一次倾斜校正和二次自校正,使失真字符接近正常字符,并利用两级模板匹配方法对字符进行快速稳定识别。
Multi-source information fusion is the technique that utilizes different information from the same object synthetically in order to obtain the more objective and entitative information for this object. Multi-source information fusion technique had its beginnings the early 1970's firstly. Ever since its emergence, this technique has made a strong impact on entire information science, such as image fusion. At the same time, studies show that vision information achieves 75% in all the information received by human. Images contain rich information, and how to obtain this information and utilize it synthetically by effective method is important subject in the pattern recognition field.
This dissertation applies the idea of multi-source information fusion. The whole dissertation is closely linked to edge detection based on fusing multiple information, secondary edge feature extraction and register multiple images based on edge feature. The edge detection and image registration are the two main topics in this dissertation. Image edge is the most important information in image, and edge detection is the basis for the follow-up work generally. And image registration belongs to image pattern recognition research field. And the image registration technique is used mainly for multiple images fusion and has practical significance in the field of military and remote sensing.
The work in the dissertation is focus on the studying of edge detection, secondary edge feature extraction and image registration. The main goal is to improve the stability and real-time performance. And this study is applied to AGVS, SAR image and numeral character recognition, etc. The major contributions in the dissertation are as follows:
(1) To overcome the contradiction between multi-dimensional vector and small training set, element correlation in vectors, computational efficiency when applying Bayesian statistical inference theory, an improved dimensional reduction method is proposed for multi-dimensional data space. The reduction direction computation is robust to outliers by introducing weight function and adjustment factor. Absolute gradient and relative gradient at several scales are constructed to be the multi-dimensional image information. Firstly, the multi-dimensional image information is reduced. Then Bayesian statistical inference theory is employed to fuse multiple features to accomplish edge detection. This method is applied to edge detection in complex scene and guided line detection in AGVS.
(2) With the research on DS evidence theory, an improved relevant evidence fusion algorithm is proposed. This method overcomes overestimation with evidence fusion, improve the reasonableness of fusion result and suitable for multiple relevant evidence fusion. Correlation degree is computed by the contribution of evidence reliability. Then a novel edge detection model based on evidence theory is also constructed. This edge detection method is used to fuse ROA operator responses and gradient operator responses at two scales in order to realize SAR image edge detection. The major advantage of this method is fusing information directly to realize edge detection without learning process.
(3) A novel image registration method based on edge fitting is proposed. This method is based on edge feature and fit these edges to straight line feature which can be expressed with mathematical method easily through an alterable precision fitting algorithm. Based the relationship of edge and image registration, straight line is screened and set a reliability value. And a weighted voting algorithm is employed to perform registration process to ensure the registration performance.
(4) According to the character of image registration, traditional Hasudorff distance is transformed to a normalized similarity value.. The traditional edge curvature computation is adjusted in order to extract the edge corners more stable. "Local jet", edge geometry and distance sequence restriction are used to improve the computation efficiency. Improved Hausdorff distance and morphological method are employed to perform registration process. This registration method is applied to route mark recognition in AGVS, SAR and aerial images registration. Experiments show that this registration method is effective and feasible.
(5) Image template matching is the spectral form of image registration. For the two application background of characters on steel billets and working station mark in AGVS, two novel numeral character recognition methods are proposed. For characters on steel billets, classical template matching technique was improved by membership assignment conception in order to improve the recognition stability. For working station mark in AGVS, first skew rectify and second own rectify are proposed to make distorted character closed to natural character. Then the two stage template matching method is employed to recognize the numeral guide character fast and stably.
引文
[1]贾永红.计算机图像处理与分析[M].武汉:武汉大学出版社,2001.
[2]赵荣椿.数字图像处理导论[M].西安:西北工业大学出版社,1995.
[3]White F E.A model for data fusion[C].Proceeding of First National Symposium on Sensor Fusion.Orlando,USA:[s.n.],1998:5-8.
[4]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社.2000.
[5]Luo L C,Kay M G.Multisensor integration and fusion for intelligent machines and system[M].New Jersey:Ablex Publishing Corporation,1994.
[6]韩崇昭,朱洪艳,段战胜.多元信息融合[M].北京:清华大学出版社,2006.
[7]Gunatilaka A H,Baertlein B A.Feature-level and decision-level fusion of non-coincidently sampled sensors for land mine detection[J].IEEE Transactions on Pattern Analysis and Machine Vision,2001,23(6):577-589.
[8]Foresti G L,Regazzoni C S.Multisensor data fusion for autonomous vehicle navigation in risky environments[J],IEEE Transactions on Vehicular Technology,2002,51(5):1165-1185.
[9]廖传锦,黄席摧,柴毅.基于信息融合的汽车防撞决策系统研究[J].系统仿真学报,2004,16(7):1589-1593.
[10]李军远,陈宏钧,张晓华,邓宗全.基于信息融合的管道机器人定位控制研究[J].控制与决策,2006,6(21):661-665.
[11]Song X B,Mostafa Y A,Sill J.Robust image recognition by fusion of contextual information[J].Information Fusion,2002,3(4):277-287.
[12]陶冰洁.多源图像配准与融合方法研究[D],成都:中国科学院光电技术研究所,2006.
[13]倪国强.多波段图像融合算法研究及其新发展[J].光电子技术与信息,2001,14(5):11-17.
[14]Poggio T,Voorhees H,Yuille A.A regularized solution to edge detection[R].USA,MA:MIT AI Laboratory,1985.
[15]章毓晋.图像分割[M].北京:科学出版社,2001.
[16]Rajab M I,Woolfson M S,Morgan S P.Application of region based segmentation and neural network edge detection to skin lesions[J],Computerized Medical Imaging and Graphics,2004,28(1):61-68.
[17]Tang H,Wu E,Ma Q,Gallagher D,Perera G M,Zhuang T.MRI brain image segmentation by multi-resolution edge detection and region selection[J],Computerized Medical Imaging and Graphics,2000,24(6):349-357.
[18]王东峰。多模态和大型图像配准技术研究[D].北京:中国科学院电子学研究所,2002.
[19]Schowengerdt R A.Remote sensing:models and methods for image processing [M].Boston:Academy Press,1997.
[20]魏伟波,芮筱亭.图像边缘检测方法研究[J].计算机工程与应用,2006,42(30):88-91.
[21]Mallat S,Zhong S,Characterization of signals from multiscale edges[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(7):710-732.
[22]Mallat S,A theory for multiresolution signal decomposition:the wavelet representation[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[23]Russo F.A New Class of Fuzzy Operators for Image Processing:design and implementation[C],Proceedings of the Second IEEE International Conference on Fuzzy Systems,San Francisco:IEEE press,1993:815-820.
[24]F.Russo,A user-friendly research tool for image processing with fuzzy rules,Proceedings of the First IEEE International Conference on Fuzzy Systems,San Diego:IEEE press,1992,561-568.
[25]胡守仁.神经网络应用技术[M].长沙:国防科技大学出版,1993.
[26]Mandelbrot B B.The fractal geometry of nature[M].New York:W.H.Freeman and Company,1983.
[27]李映,焦李成.基于自适应免疫遗传算法的边缘检测[J].计算机图象处理,2003,8(8):890-895.
[28]郑南宁.计算机视觉与模式识别[M],北京:国防工业出版社,1998.
[29]Bertero M,Poggio T A.Ill-posed problems in early vision[J].proceedings of the IEEE,1998,76(8):869-889.
[30]Poggio T,Torre V.Ill-posed problems and regularization analysis in early vision[C].Proceedings of ARPA Image Understanding Workshop,MA,USA:Massachusetts Institute of Workshop,1984:257-263.
[31]Torre V,Poggio T.On edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(2):147-163.
[32]Yuille A P,Poggio T A.Scaling theorems for zero-crossings.IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(1):15-25.
[33]Robert L G.Machine perception of three-dimensional solids[C].Optical and Electro-Optical Information Processing,Cambridge,MA:MIT Press,1965:159-197.
[34]Ando S.Consistent gradient operators[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(3):252-265.
[35]段瑞玲,李庆祥,李玉和.图像边缘检测方法研究综述[J].光学技术,2005,31(3):415-419。
[36]Prewitt J.Object enhancement and extraction,in picture proc,and pcychopictories[M].New York:Academic,1970.
[37]Kirsch R.Computer determination of the constituent structure of biological images[J].Computer Biomedical Research,1971,4(3):315-328.
[38]Clark J J.Authenticating edges produced by zero-crossing algorithm[J].IEEE Transcations on Pattern Analysis and Machine Intelligence,1998,20(1):43-57.
[39]Marr D,Hildreth E C.Theory of edge detection[J].Proceedings of the Royal Society,1980,B(27):187-217.
[40]Canny J.A computational approach to edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[41]Deriche R.Using Canny's criteria to derive a recursively implemented optimal edge detection[J].The International Journal of Computer Vision,1987,12(1):67-187.
[42]Shen J,Castan S.A new algorithm for edge detection[C].In P rocessings 5th Conference on PR &AI,Grenoble,France:[s.n.],1985:73-75.
[43]Shen J,Castan S.A n optimal linear operator for step edge detection[J].CV GIP Graph icalModels and Image P rocess,1992,(54):112-133.
[44]Shen J.On multi edge detection[J].CV GIP Graphical Models and Image Process,1996,(58):101-114.
[45]Liang K H,Tjahjadi T,Yang Y H.Roof edge detection using regularized cubic B-spline fiting[J].Patern Recognition,1997,30(5):719-728
[46]A.Coshtasy.B-Spine surfaces viewed as digital filters[J].CV GIP Graphical Models and Image Process,I990,52:264-275.
[47]Haralick R.Digital step edges from zero crossing of second directional derivatives[J].IEEE Transcations on Pattern Analysis and Machine Intelligence,1984,6(1):58-68.
[48]Haralick R.Edge and region analysis for digital image data,in image modeling [M],New York:Academic Press,1981.
[49]Mallat S G.Multifrequency channel decomposition of images and wavelet models,IEEE Trans on Acoustics Speech Signal Processing,1989,37(12):2091-2110.
[50]Chui C K.An introduction to wavelets[M],Boston:Academic Press,1992.
[51]Mallat S G,Huang W L.Singularity detection and processing with wavelets[J].IEEE Transactions on Information Theory,1992,38(2):617-643.
[52]许传祥,石青云,程民德.零对称和反对称二进小波及其在边缘检测中的应用[J].中国图象图形学报,1996,1(1):4-11.
[53]章国宝,叶桦.基于正交小波变换的多尺度边缘提取[J].中国图象图形学报,1998,3(8):651-654.
[54]赵志刚,管聪慧.基于多尺度边缘检测的自适应阈值小波图像降噪[J].仪器仪表学报,2007,28(2):288-292.
[55]Dusan H,Damjan Z.Combined edge detection using wavelet transform and signal registration[J].Image and Vision Computing,2007,25(5):652-662.
[56]杜慧茜,梅文博,苏广川.基于统计处理和B样条小波变换的毫米波图像边缘检测[J].中国图象图形学报,2005,10(11):1145-1149.
[57]Wang Y P,Cai Y L.Construction and properties of B-spline wavelet filters for multiscale edge detection[C].IEEE International Conference on Image Processing,Los Alamitos:IEEE Computer Society Press,1995:145-148.
[58]杨晓梅,朱长青.多进制小波变换及其在影像分析中的应用[J].中国图象图形学报,1999,4(2):157-160.
[59]贾天旭,郑南宁.基于Bubble小波的多尺度边缘提取[J],电子学报,1996,24(4):117-121.
[60]Chui C K,Lian J A.A construction of compactly supported symmetric and orthonormal wavelets with scale=3[J].Applied and Computational Harmonic Analysis,1995,2(1):21-52.
[61]Hildreth E C.The detection of intensity changes by computer and biological vision system[J].CV GIP Graphical Models and Image Process.1983,22(1):1-27.
[62]Xin Y,Boekaerts P,Bister M,Taeymans Y,Cornelis J.Left ventricular boundary detection in echocardiographic images using artificial neural networks[C].6th European Signal Processing Conference,Brussels,Belgium:Elsevier Science Publishers,1992:1199-1202.
[63]Pen B S,Gordon A J,Wendlandt R F.Using neural networks to locate edges and linear features in satellite images[J].Computers and.Geosciences,1993,19(10):1545-1565.
[64]Spreeuwers L J.Neural network edge detector[C].Proc SPIE,San Jose,CA, USA:SPIE press,1991:204-210.
[65]Dhawan A P,Dufresenr T.Low-level image processing and edge enhancement using a self-organizing neural network[C].International Joint Conference on Neural Networks,San Diego,CA,USA:IEEE press,1990:503-510.
[66]Xue K.Breznik C W.A neural-net computing algorithm for detecting edges in a gray scale image[C].Proceedings of the 29th IEEE Conference on Decision and Control,Honolulu,HI,USA:IEEE press,1990,2368-2373.
[67]杨海军,梁德超.一种新的基于信息测度和神经网络德边缘检测方法[J].电子学报,2001,29(1):51-53.
[68]张晶,张权,王欣.一种新的基于统计向量和神经网络的边缘检测方法[J].计算机研究与发展,2006,43(5):920-920.
[69]罗希平,田捷,诸葛婴.图像分割方法综述[J].模式识别与人工智能,1999,12(3):300-312.
[70]Goldberg D E.Genetic algorithms in search,optimization,and machine learning [M].MA:Addison-Wesley press,1989.
[71]Bhandarkar S M,Zhang Y Q,Potter W D.Genetic algorithm-based edge detection technique[C].Proceedings of 1993 International Joint Conference on Neural Networks,Nagoya,Jpn:IEEE press 1993:2995-2999.
[72]李映,焦李成.边缘检测的混合量子遗传算法[J].模式识别与人工智能,2003,16(2):219-224.
[73]Vapnik V.The nature of statistical learning theory[M].New York:wiley,1998.
[74]郑胜,柳健,田金文.基于向量机的边缘检测算法优化研究[J].电子与信息学报,2005,27(5):717-721.
[75]王珮,张艳宁,申家振,刘俊成.基于信息测度和支持向量机的图像边缘检测[J].山东大学学报(工学版),2006,36(3):95-99.
[76]Kundu A.Robust edge detection[J].Pattern Recognition,1990,23(5):423-440.
[77]Pal S K,King R A.On edge detection of x-ray images using fuzzy sets[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1983,5(1):69-77.
[78]Marmdani E H.Advances in the linguistic synthesis of fuzzy controller[J].International Journal of Man-Machine Studies,1976,8(6):669-679.
[79]周德龙,潘泉.图像模糊边缘检测的改进算法[J].中国图象图形学报.2001,6(4):353-358.
[80]王倩,阮海波.快速模糊边缘检测算法[J].中国图象图形学报,2001,6(1):92-95。
[81]Russo F.Edge detection in noisy images using fuzzy reasoning[J].IEEE Transactions on Instrumentation and Measurement,1998,47(5):1102-1105.
[82]陈波,杨阳,沈田双.基于模糊聚类分析的边缘检测算法[J].仪器仪表学报,2006,27(6):1603-1604.
[83]杨华千,张伟,韦鹏程,陈军.基于小波多尺度变换和模糊聚类的图像边缘检测研究[J].计算机科学,2006,33(1):274-276.
[84]Xu C.Analysis of morphological quasi-circular structure elements and skeleton [J].Acta Electronica Sinica,1999,27(8):78-81
[85]Maragos P.Tutorial on advances in morphological image processing and analysis.[J].Optical Engineering.1987,26(7):623-632
[86]Haralick R,Zhuang X.Image analysis using mathematical morphology.[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(4):532-550.
[87]Li X J.Mathematical morphological edge detectors for noisy image corrupted by impulses.[J].Journal of Image and Graphics.1998,3(11):903-906
[88]Liang Y.Application of multi-orientation morphological structure elements in edge detection.[J].J.Yunnan University,1999,21(25):392-394
[89]Todd L,Hirohisa S.Image filtering,edge detection and edge tracing using fuzzy reasoning.[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(5):481-491.
[90]Lee J,Haralick R M,Shapiro L G.Morphology Edge Detection[J].IEEE Journal of Robotics and Automation,1987,3(2):142-156.
[91]杨述斌,彭复员.噪声污染图象中的广义形态边缘检测器[J].计算机工程与应用,2002,38(17):91-92.
[92]杨述斌,彭复员,张增常.多尺度自适应加权形态边缘检测器[J].华中科技大学学报,2002,30(10):42-45.
[93]杨述斌.数学形态学在图象处理中的应用研究[D].武汉:华中科技大学,2002.
[94]冉鑫.基于可变形模型的图像分割与图像配准研究[D],上海:上海交通大学,2005.
[95]Simper A.Correcting general band-to-band misregistrations.Proceedings of the 1996 IEEE International Conference on Image Processing,Lausanne,Switz:IEEE press,1996:597-600.
[96]Irani M,Anadan P.All about direct methods[C].Proceedings of International Workshop Vision Algorithms,Corfu,Greece:IEEE press,1999:267-277.
[97]Rosenfeld A,Kak A C.Digtal Picture Processing[M].New York:Academic Press,1982.
[98]Prat W K,Digital Image Processing[M].New York:Wiley press,1991.
[99]Barnea D I,Silverman H F.A class of algorithms for fast digital image registration[J].IEEE Transactions on Computing,1972,21(2):179-186.
[100]沈庭芝,方子文.数字图像处理及模式识别[M].北京:北京理工大学出版社,1998.
[101]A.Collignon,F.Maes,D.Delaere,D.Vandermeulen,P.Suetens,G.Marchal,Automated multi-modality image registration based on information theory[C],Proceedings of the 1995 14th International Conference on Information Processing in Medical Imaging,Ile de Berder,France:[s.n.],1995,263-274.
[102]Viola P A,Wells W M,Alignment by maximization of mutual information[J].International Journal of Computer Vision,1997,24(2),137-154.
[103]Shannon C E.A mathematical theory of communication[J].Bell Systems technical Journal,27:379-423,623-656,1948.
[104]Zitova B,Flusser J.Image registration methods:a survey[J].Image and Vision Computing,2003,24:977-1000.
[105]陈昱,庄天戈.基于互信息非刚性医学图像配准的方法[J].上海交通大学学报,1999,33(9):1125-1127.
[106]罗述谦,李响.基于最大互信息的多模医学图象配准[J].中国图象图形学报,2000,5A(7):551-558.
[107]C.D.Kuglin and D.C.Hines,The phase correlation image alignment method [C].IEEE International Conference on Cybernetics and Society,New York,USA:IEEE press,1975:163-165.
[108]Alliney S.Spatial registration of multispectral and multitemporal digital imagery using fast-Fourier transform techniques[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(5),499-504.
[109]Lee D J,Krile T F,Mitra S.Digital registration techniques for sequential fundus images[C].IEEE Proc of SPIE:Applications of Digital Image Processing,New York,USA:IEEE press,1987:293-300.
[110]Reddy B S,Chatterji B N.An FFT-based technique for translation,rotation,and scale-invariant image registration[J].IEEE Transactions on Image Processing,1996,5(8),1266-1271.
[111]Mone J L.Parallel registration of multisensor remotely sensed imagery using wavelet coeficients[C].Proceeding of SPIE O/E Aerospace Sensing,Wavelet Applications,1994:432-443.
[112]Chen Q,Defrise M,Deconinck F,Symmetric phase-only matched filtering of Fourier-Mellin transform for image registration and recognition[J].IEEE Transactions on Patern Analysis and Machine Intellingence,1994,16(12):1156-1168.
[113]Bracewell R N,Chang K Y,Jha A K,Wang Y H,Affine theorem for two-dimensional Fourier transform[J].Electronics Letters,1993,29(3):304-309.
[114]刘卫光,崔江涛,周利华.插值和相位相关的图像亚像素配准方法[J].计算机辅助设计与图形学学报,2005,17(6):1273-1277.
[115]Harold S,Michael T,Orchard,Chang E C,Stephen A.Martucci,A fast direct Fourier-based algorithm for subpixel registration of images[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(10),2235-2243
[116]Shekarforoush S,Berthed M,Zerubia J.Subpixel image registration by the estimating and Pattern polyphase decomposition of cross power spectrum[C].Proceedings of the 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Francisco,CA,USA:IEEE press,1994:532-537.
[117]叶发茂.基于人工免疫系统的多源遥感图像配准研究[D].北京:中国科学院遥感应用研究所,2006.
[118]Yang Z W,Femand S.Image registration and object recognition using affine invariants and convex hulls[J].IEEE Transactions on Image Processing,1999,8(7):934-946.
[119]Chang S H,Cheng F G,Hsu W H.Fast algorithm for point pattern matching:invariant to translation,rotations and scale changes[J].Patterm Recognition,1997,30(2):311-320.
[120]Dufournaud Y,Schmid C,Horaud R.Image matching with scale adjust[J].Computer Vision and Image Understanding,2004,93(2):175-194.
[121]Koenderink J J,Van Doom A J.Representation of local geometry in the visual system[J].Biological Cybernetics,1987,55(6):367-375.
[122]仵建宁,郭宝龙,冯宗哲.一种基于兴趣点特征匹配的图像镶嵌技术[J],光电子.激光,2006,17(6):733-737.
[123]Goshtasby A.A symbolically-assisted approach to digital registration with application in computer vision[D].Michigan:Michigan State University,1983.
[124]Goshtasby A,Stockman G C,Page C V.A region-based approach to digital image registration with subpixel accuracy[J].IEEE Transactions on Geoscience and Remote Sensing,1986,GE-24(3):390-399.
[125]Ton J,Jain A K.Registering Landsat images by point matching[J].IEEE Transactions on Geoscience and Remote Sensing,1989,27(5):642-651.
[126]薛模根,韩裕生,李从利,袁广林,冷晓艳.基于区域同名度矩阵的连续帧图像配[J].中国图象图形学报,2006,11(1):53-59.
[127]刘贵喜,王蕾.基于区域选择和特征点匹配的图像配准算法[J].光电子.激光,2007,18(8):999-1002.
[128]Bouchafa S,Zavidovique B.Efficient Cumulative matching for image registration[J],Image and Computing,2006,24:70-79.
[129]张鹏强,余旭初,韩丽,刘景正.基于直线特征匹配的序列图像自动配准[J],武汉大学学报(信息科学版),2007,32(8):676-679.
[130]Dai X L,Khorram S,A Feature-Based Image Registration Algorithm Using Improved Chain-Code Representation Combined with Invariant Moments[J],IEEE Transactions on Geoscience and Remote Sensing,1999,37(5),2351-2362.
[131]Li H,Manjunath B S,Mitra S K,A Contour-Based Approach to Mutisensor Image Registration[J].IEEE Transactions on Image Processing,1995,4(3),320-333.
[132]谌安军,陈炜,毛士艺.一种基于边缘的图像配准方法[J],电子与信息学报,2004,26(5):679-684.
[133]邓鹏,王宏琦.结合边缘与灰度信息的SAR图像配准算法[J],遥感技术与应用,2003,18(3):159-164.
[134]Field D J.Relations between the statistics and natural images and the responses properties of cortical cells[J].Optical Soc.Am.,1987,A(4):2379-2394.
[135]Wainwright M J,Simoncelli E P.Scale mixtures of gaussians and the statistics of natural images[C].Proc.Neural Information Processing Systems,Cambridge,MA:MIT press,2000:855-861.
[136]Zhu S C,Mumford D B.Prior learning and gibbs reactiondiffusion[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(11):1236-1250.
[137]Bhattacharyya A.On a measure of divergence between two statistical populations defined by their probability distribution[J].Bulletin of the Calcutta Mathematical Society,1943,35:99-119.
[138]Chernoff H.A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations[J].Annals of Mathematical Statistics,1952,23(4):493-507.
[139]Bowyer K,Kranenburg C,Dougherty S.Edge detector evaluation using empirical ROC curves[J].Computer Vision and Image Understanding,2001,84(1):77-103.
[140]Liu J,Li.B,Dillon T S.An improved naive bayesian classifier technique coupled with a novel input solution method[J].IEEE Transactions on Systems,Man,and Cybernetics,2001,31(2):249-256.
[141]Henley W E,Hand D J.A k-nearest-neighbour classifier for assessing consumer credit risk[J].The Statistician,1996,45(1):77-95.
[142]Parzen E.On estimation of a probability density function and mode[J].Annals of Mathematical Statistics,1962,33(3):1065-1076.
[143]Scott K,Yuille A L,James M C,Song C Z.Statistical edge detection:learning and evaluating edge cues[J].IEEE Transactions on Pattern Analysis and Machine Intellihence,2003,25(1):57-74.
[144]Cover T M,Thomas J A.Elements of information theory[M].New York:Wiley Interscience Press,1991.
[145]Huertas A.Medioni G.Detection of intensity changes with subpixel accuracy using laplacian of Gaussian masks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(5):651.
[146]Touzi R,Lopes A,Bousquet P.A statistical an d geometrical edge detector for SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,1988,26(6):764-773.
[147]Oliver C J,Biacknell D,White R G.Optimum edge detection in SAR[J].IEEE Proceedings of Radar Sonar Navigation,1996,143(1):31-40.
[148]Benoit T,Bertrand A.A fast multi-scale edge detection algorithm[J].Pattern Recognition Letters,2004,25(6):603-618.
[149]Shafer G.A Mathematical theory of evidence[M],Princeton University Press,Princeton,1976.
[150]Bellman R,Zadeh L A.Decision making in a fuzzy environment[J].Management Science,1970,17(4):141-164.
[151]肖人彬,王雪,费奇.相关证据合成方法的研究[J].模式识别与人工智能,1993,6(3):227-234.
[152]Ulaby F T,Kouyyate F,Brisco B,Williams T.Textural information in SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,1986,24(2):235-245.
[153]Goodman J W.Some fundamental properties of speckle[J].Journal of the Optical Society of America,1976,66(11):1145-1150.
[154]Lopes A,Touzi R.Adaptive speckle filters and scene heterogencity[J].IEEE Transactions on Geoscience and Remote Sensing,1990,28(6):992-1000.
[155]Zaman M R,Moloney C R.A comparsion of adaptive filters for edge-preserving smppthing of speckle noise[C].IEEE International Conference on Acoustics,Speech and Signal Processing,Minneapolis,MN,USA:IEEE press,1993:77-80.
[156]Kenneth R C.Digital image processing[M].Beijing:Publishing House of Electronics Industry,1999.
[157]Hong Y.Unified formulation of a class of image thresholding techniques[J].Pattern Recognition,1996,29(12):2025-2032.
[158]Abdou I E,Pratt W K.Quantitative design and evaluation of enhancement/thresholding edge detectors[J].Proceedings of the IEEE,1979,67(5):753-763.
[159]Brown L G.A survey of image registration techniques[J].ACM Computing Surveys,1992,24(4):325-376.
[160]Caves R C,Harley P J,Quegan S.Matching map feature to synthetic aperture radar image using template matching[J].IEEE Transactions On Geoscience And Remote Sensing,1992,30(4):680-685.
[161]Thevenaz P,Unser M.Optimization of mutual information for multiresolution image registration[J].IEEE Transactions On Image Processing,2000,9(12):2083-2099.
[162]谌安军,陈炜,毛士艺.一种基于边缘得图像配准方法[J].电子与信息学报,2004,26(5):679-684.
[163]Medioni G G,Nevatia R.Matching images using linear features[J].IEEE Transactions on Pattern Analysis and Mechine Intelligence,1984,6(6):675-685.
[164]Kuhl P,Giardina C.Elliptic fourier features of a closed contour[J].Computer Graphics and Image Processing,1982,18(3):236-258.
[165]Dufournaud Y,Dchmid C,Horaud R.Image matching with scale adjustment[J].Computer Vision and Image Understanding,2003,93(2):175-194.
[166]Rucklidge W J.Eficiently locating objects using the hausdorf distance[J].International Journal of Computer Vision,1997,24(3):251-270.
[167]Guilloux Y.A matching algorithm for horizontal Motion,application to tracking [C].IEEE Proceeding of the 8th International Conference on Pattern Recognition,Pairs,France:IEEE Computer Society Press,1986:1190-1192.
[168]Szeliski R.Image mosaicing for tele-reality application[C].Proceedings of IEEE workshop on applications of computer,Piscataway,USA:IEEE Computer Society Press,1994:54-62.
[169]Ranade S,Rosenfeld A.Point pattern matching by relaxation[J].Pattern Recognition,1980,12(4):269-275.
[170]Choo C Y,Freeman H.An efficient technique for compressing chain-coded line drawing images[C].Signals,Systems and Computers,1992,2:717-720.
[171]陈振林.基于傅立叶描绘子的实时图像识别系统[J].仪器仪表学报,2003,24(4):391-394.
[172]Hu M K.Visual pattern recognition by moment invariants.[J].IEEE Transactions on Information Theory,1962,8(2):179-187.
[173]朱庆,吴波,万能,徐志祥,田一翔.具有良好重复率与信息量的立体影像点特征提取方法[J].电子学报,2006,34(2):205-209.
[174]Some W L.Terms of reference in data fusion[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(3):1190-1193.
[175]Pohl C,Van Genderen J.Multisensor image fusion in remote sensing:concepts,methods and applications[J].International Journal of Remote Sensing,1998,19(2):823-854.
[176]Harris C,Stephens M.A combined comer and edge detector[C].Proceedings of 4th Alvey Vision Conference,Manchester,UK:University of Manchester Press,1988:147-151.
[177]Huttenlocher D P,Klanderman G A,Rucklidge W J.Comparing images using the Hausdorff distance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(9):850-863.
[178]Borgevors G.Distance transforms in digital image[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,34(3):344-371.
[179]Dubuisson M P,Jain A K.A modified Hausdorff distance for object matching [C].Proceedings of the 12th IAPR International Conference on Pattern Recognition,Jerusalem,Isr:IEEE press,1994,566-568.
[180]Lu Y,Tan C.Chinese word searching in imaged documents[J].International Journal of Pattern Recognition and Artificial In telligence,2004,18(2):229-246.
[181]Lin K H,Lain K M,Siu W C.Spatially eigen-weighted hausdorff distances for human face recognition[J].Pattern Recognition,2003,36(8):1827-1834.
[182]Guo B F,Lam K M,Siu W C,Yang S.Human face recognition using a spatially weighted Hausdorff distance[C].IEEE International Symposium on Circuits and Systems,Sydney,NSW:Institute of Electrical and Electronics Engineers Inc,2001:145-148.
[183]Azencott R,Durbin F,Paumard J.Multiscale indentification of building in compressed aerial scenes[C].Proceedings of 13th International Conference on Pattern Recognition,Vienna,Austria:[s.n.],1996,974-978.
[184]Sire D G,Kwon O K,Park R H.Object matching algorithm using robust Hausdorff distance measures[J].IEEE Transactions on Image Processing,1999,8(3):425-429.
[185]汪亚明.图像匹配的鲁棒型Hausdorff方法[J].计算机辅助设计与图形学学报,2002,14(3):1-4.
[186]Li Y,Robert L,Stevenson,Cai J D.Comer-guided image registration by using edges[C].IEEE International Conference on Image Processing,San Antonio,Texas,USA:IEEE press,2007:361-364.
[187]黄天云,孙世新,庞朝阳.基于Gauss滤波的多媒体图像的可视化检索[J].系统工程与电子技术,2001,23(1):77-81.
[188]Koenderink JJ.The structure of images[J].Biological Cybernetics,1984,50(5):363-370.
[189]Paolo C.Optical recognition of motor vehicle license plates[J].IEEE Transactions on Vehicular Technology,1995,44(4):790-799.
[190]Wen Y,Lu Y,Shi P.Handwritten Bangla numeral recognition system and its application to postal automation[J].Pattern Recognition,2007,40(1):99-107
[191]尹朝庆,宋化,陈波.手写邮政编码的模糊识别方法[J].武汉理工大学学报(信息与管理工程版),2005,25(2):154-157.
[192]Paquet T,Lecourtier Y,Handwritten recognition:application on bank cheques [C],Proceedings of the First International Conference on Document Analysis and Recognition,Saint-Malo,France:IEEE Computer Society Press,1991:749-757.
[193]Guillevic D,Suen C y,HMM-KNN word recognition engine for bank cheque processing[C],Proceedings of the 14th International Conference on Pattern Recognition,Brisbane,Australia:IEEE Computer Society Press,1998:1526-1529.
[194]芮明,路通,苏丰,蔡士杰.基于视觉的表格自动识别方法[J].计算机应用研究,2005,22(4):256-257.
[195]刘强,晏蒲柳,郭成城,邹华.基于脊线校准的指纹点匹配算法[J].武汉大学学报(理学版),2003,49(1):102-106.
[196]俞能海,陈豪,刘政凯.基于块金字塔的快速匹配算法[J].电路与系统学 报,2003,8(2):54-57.
[197]汤泽滢,卢汉清,罗建书.基于解析形式的二维参数可变形模板匹配算法[J].中国图象图形学报,2004,9(7):775-780.
[198]朱昊,刘文耀,王金涛,郝永杰,薛白.汽车牌照的字符分割与识别[J].计算机测量与控制,2003,11(8):574-576.
[199]Chim Y C,Kassim A A,Ibrahim Y.Character recognition using statistical moments[J].Image and Vision Computing,1999,17(3):299-307.
[200]吴谨,刘洋.具有抗噪稳健性的数字识别[J].武汉科技大学学报(自然科学版),2004,27(4):406-408.
[201]Parisi R,Claudio E,Lucarell G.Car plate recognition by neural networks and image processing[J].IEEE Transaction on Neural Networks,1998,7(2):195-198.
[202]薛磊,杨晓敏,吴炜,陈默,何小海.一种基于KNN与改进SVM的车牌字符识别算法[J].四川大学学报(自然科学版),2006,43(5):1031-1036.
[203]Frigui H,Gader P,Krishnapuram R.Handwritten character membership function estimation for word recognition[J].Fuzzy Systems,2001,3(2):928-931.
[204]Turksen I B,Kandel A,Zhang Y Q.Normal forms of fuzzy middle and fuzzy contradictions[J].IEEE Transactions on Systems,Man,and Cybernetics,1999,29(2):237-253.
[205]王希雷,王磊.车牌识别中基于Rough集理论的字符识别[J].计算机工程,2006,32(24):204-205.
[206]陈继荣,黄戈.ICA和改进的SVM在有限集字符识别中的应用[J].计算机工程,2006,32(23):199-201.
[207]虞安军,吴海珍,蒋加伏.改进的遗传算法在车牌自动识别系统中的应用[J].计算机仿真,2006,23(11):224-227.
[208]蔡晋辉,张光新,周泽魁.面积重构H顶变换的实现与应用[J].光电工程,2005,32(3):73-77.
[209]张玉姣,史忠科.基于连通体检测及投影法的牌照字符切分[J].小型微型计算机系统,2004年,25(4):564-566.
[210]Hegt H A,Haye R J,Khan N A.A high performance license plate recognition system[C].IEEE International Conference on Systems,Man and Cybernetics,San Diego California,USA:IEEE Computer Society Press,1998:4357-4362.
[211]阀伟,朱秋煜.一种改进的基于模板匹配的集装箱字符识别方法[J].计算机工程,2000,26(12):119-120,143.
[212]吴炜,余艳梅,刘大宇,何小海.一种基于Hausdorff距离的车牌字符识别算法[J].计算机应用研究,2004,21(2):258-260.
[213]吴谨,邱亚.基于空间分布特征的手写体数字识别[J].武汉科技大学学报(自然科学版),2004,27(2):176-178.
[2]赵荣椿.数字图像处理导论[M].西安:西北工业大学出版社,1995.
[3]White F E.A model for data fusion[C].Proceeding of First National Symposium on Sensor Fusion.Orlando,USA:[s.n.],1998:5-8.
[4]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社.2000.
[5]Luo L C,Kay M G.Multisensor integration and fusion for intelligent machines and system[M].New Jersey:Ablex Publishing Corporation,1994.
[6]韩崇昭,朱洪艳,段战胜.多元信息融合[M].北京:清华大学出版社,2006.
[7]Gunatilaka A H,Baertlein B A.Feature-level and decision-level fusion of non-coincidently sampled sensors for land mine detection[J].IEEE Transactions on Pattern Analysis and Machine Vision,2001,23(6):577-589.
[8]Foresti G L,Regazzoni C S.Multisensor data fusion for autonomous vehicle navigation in risky environments[J],IEEE Transactions on Vehicular Technology,2002,51(5):1165-1185.
[9]廖传锦,黄席摧,柴毅.基于信息融合的汽车防撞决策系统研究[J].系统仿真学报,2004,16(7):1589-1593.
[10]李军远,陈宏钧,张晓华,邓宗全.基于信息融合的管道机器人定位控制研究[J].控制与决策,2006,6(21):661-665.
[11]Song X B,Mostafa Y A,Sill J.Robust image recognition by fusion of contextual information[J].Information Fusion,2002,3(4):277-287.
[12]陶冰洁.多源图像配准与融合方法研究[D],成都:中国科学院光电技术研究所,2006.
[13]倪国强.多波段图像融合算法研究及其新发展[J].光电子技术与信息,2001,14(5):11-17.
[14]Poggio T,Voorhees H,Yuille A.A regularized solution to edge detection[R].USA,MA:MIT AI Laboratory,1985.
[15]章毓晋.图像分割[M].北京:科学出版社,2001.
[16]Rajab M I,Woolfson M S,Morgan S P.Application of region based segmentation and neural network edge detection to skin lesions[J],Computerized Medical Imaging and Graphics,2004,28(1):61-68.
[17]Tang H,Wu E,Ma Q,Gallagher D,Perera G M,Zhuang T.MRI brain image segmentation by multi-resolution edge detection and region selection[J],Computerized Medical Imaging and Graphics,2000,24(6):349-357.
[18]王东峰。多模态和大型图像配准技术研究[D].北京:中国科学院电子学研究所,2002.
[19]Schowengerdt R A.Remote sensing:models and methods for image processing [M].Boston:Academy Press,1997.
[20]魏伟波,芮筱亭.图像边缘检测方法研究[J].计算机工程与应用,2006,42(30):88-91.
[21]Mallat S,Zhong S,Characterization of signals from multiscale edges[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(7):710-732.
[22]Mallat S,A theory for multiresolution signal decomposition:the wavelet representation[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[23]Russo F.A New Class of Fuzzy Operators for Image Processing:design and implementation[C],Proceedings of the Second IEEE International Conference on Fuzzy Systems,San Francisco:IEEE press,1993:815-820.
[24]F.Russo,A user-friendly research tool for image processing with fuzzy rules,Proceedings of the First IEEE International Conference on Fuzzy Systems,San Diego:IEEE press,1992,561-568.
[25]胡守仁.神经网络应用技术[M].长沙:国防科技大学出版,1993.
[26]Mandelbrot B B.The fractal geometry of nature[M].New York:W.H.Freeman and Company,1983.
[27]李映,焦李成.基于自适应免疫遗传算法的边缘检测[J].计算机图象处理,2003,8(8):890-895.
[28]郑南宁.计算机视觉与模式识别[M],北京:国防工业出版社,1998.
[29]Bertero M,Poggio T A.Ill-posed problems in early vision[J].proceedings of the IEEE,1998,76(8):869-889.
[30]Poggio T,Torre V.Ill-posed problems and regularization analysis in early vision[C].Proceedings of ARPA Image Understanding Workshop,MA,USA:Massachusetts Institute of Workshop,1984:257-263.
[31]Torre V,Poggio T.On edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(2):147-163.
[32]Yuille A P,Poggio T A.Scaling theorems for zero-crossings.IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(1):15-25.
[33]Robert L G.Machine perception of three-dimensional solids[C].Optical and Electro-Optical Information Processing,Cambridge,MA:MIT Press,1965:159-197.
[34]Ando S.Consistent gradient operators[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(3):252-265.
[35]段瑞玲,李庆祥,李玉和.图像边缘检测方法研究综述[J].光学技术,2005,31(3):415-419。
[36]Prewitt J.Object enhancement and extraction,in picture proc,and pcychopictories[M].New York:Academic,1970.
[37]Kirsch R.Computer determination of the constituent structure of biological images[J].Computer Biomedical Research,1971,4(3):315-328.
[38]Clark J J.Authenticating edges produced by zero-crossing algorithm[J].IEEE Transcations on Pattern Analysis and Machine Intelligence,1998,20(1):43-57.
[39]Marr D,Hildreth E C.Theory of edge detection[J].Proceedings of the Royal Society,1980,B(27):187-217.
[40]Canny J.A computational approach to edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[41]Deriche R.Using Canny's criteria to derive a recursively implemented optimal edge detection[J].The International Journal of Computer Vision,1987,12(1):67-187.
[42]Shen J,Castan S.A new algorithm for edge detection[C].In P rocessings 5th Conference on PR &AI,Grenoble,France:[s.n.],1985:73-75.
[43]Shen J,Castan S.A n optimal linear operator for step edge detection[J].CV GIP Graph icalModels and Image P rocess,1992,(54):112-133.
[44]Shen J.On multi edge detection[J].CV GIP Graphical Models and Image Process,1996,(58):101-114.
[45]Liang K H,Tjahjadi T,Yang Y H.Roof edge detection using regularized cubic B-spline fiting[J].Patern Recognition,1997,30(5):719-728
[46]A.Coshtasy.B-Spine surfaces viewed as digital filters[J].CV GIP Graphical Models and Image Process,I990,52:264-275.
[47]Haralick R.Digital step edges from zero crossing of second directional derivatives[J].IEEE Transcations on Pattern Analysis and Machine Intelligence,1984,6(1):58-68.
[48]Haralick R.Edge and region analysis for digital image data,in image modeling [M],New York:Academic Press,1981.
[49]Mallat S G.Multifrequency channel decomposition of images and wavelet models,IEEE Trans on Acoustics Speech Signal Processing,1989,37(12):2091-2110.
[50]Chui C K.An introduction to wavelets[M],Boston:Academic Press,1992.
[51]Mallat S G,Huang W L.Singularity detection and processing with wavelets[J].IEEE Transactions on Information Theory,1992,38(2):617-643.
[52]许传祥,石青云,程民德.零对称和反对称二进小波及其在边缘检测中的应用[J].中国图象图形学报,1996,1(1):4-11.
[53]章国宝,叶桦.基于正交小波变换的多尺度边缘提取[J].中国图象图形学报,1998,3(8):651-654.
[54]赵志刚,管聪慧.基于多尺度边缘检测的自适应阈值小波图像降噪[J].仪器仪表学报,2007,28(2):288-292.
[55]Dusan H,Damjan Z.Combined edge detection using wavelet transform and signal registration[J].Image and Vision Computing,2007,25(5):652-662.
[56]杜慧茜,梅文博,苏广川.基于统计处理和B样条小波变换的毫米波图像边缘检测[J].中国图象图形学报,2005,10(11):1145-1149.
[57]Wang Y P,Cai Y L.Construction and properties of B-spline wavelet filters for multiscale edge detection[C].IEEE International Conference on Image Processing,Los Alamitos:IEEE Computer Society Press,1995:145-148.
[58]杨晓梅,朱长青.多进制小波变换及其在影像分析中的应用[J].中国图象图形学报,1999,4(2):157-160.
[59]贾天旭,郑南宁.基于Bubble小波的多尺度边缘提取[J],电子学报,1996,24(4):117-121.
[60]Chui C K,Lian J A.A construction of compactly supported symmetric and orthonormal wavelets with scale=3[J].Applied and Computational Harmonic Analysis,1995,2(1):21-52.
[61]Hildreth E C.The detection of intensity changes by computer and biological vision system[J].CV GIP Graphical Models and Image Process.1983,22(1):1-27.
[62]Xin Y,Boekaerts P,Bister M,Taeymans Y,Cornelis J.Left ventricular boundary detection in echocardiographic images using artificial neural networks[C].6th European Signal Processing Conference,Brussels,Belgium:Elsevier Science Publishers,1992:1199-1202.
[63]Pen B S,Gordon A J,Wendlandt R F.Using neural networks to locate edges and linear features in satellite images[J].Computers and.Geosciences,1993,19(10):1545-1565.
[64]Spreeuwers L J.Neural network edge detector[C].Proc SPIE,San Jose,CA, USA:SPIE press,1991:204-210.
[65]Dhawan A P,Dufresenr T.Low-level image processing and edge enhancement using a self-organizing neural network[C].International Joint Conference on Neural Networks,San Diego,CA,USA:IEEE press,1990:503-510.
[66]Xue K.Breznik C W.A neural-net computing algorithm for detecting edges in a gray scale image[C].Proceedings of the 29th IEEE Conference on Decision and Control,Honolulu,HI,USA:IEEE press,1990,2368-2373.
[67]杨海军,梁德超.一种新的基于信息测度和神经网络德边缘检测方法[J].电子学报,2001,29(1):51-53.
[68]张晶,张权,王欣.一种新的基于统计向量和神经网络的边缘检测方法[J].计算机研究与发展,2006,43(5):920-920.
[69]罗希平,田捷,诸葛婴.图像分割方法综述[J].模式识别与人工智能,1999,12(3):300-312.
[70]Goldberg D E.Genetic algorithms in search,optimization,and machine learning [M].MA:Addison-Wesley press,1989.
[71]Bhandarkar S M,Zhang Y Q,Potter W D.Genetic algorithm-based edge detection technique[C].Proceedings of 1993 International Joint Conference on Neural Networks,Nagoya,Jpn:IEEE press 1993:2995-2999.
[72]李映,焦李成.边缘检测的混合量子遗传算法[J].模式识别与人工智能,2003,16(2):219-224.
[73]Vapnik V.The nature of statistical learning theory[M].New York:wiley,1998.
[74]郑胜,柳健,田金文.基于向量机的边缘检测算法优化研究[J].电子与信息学报,2005,27(5):717-721.
[75]王珮,张艳宁,申家振,刘俊成.基于信息测度和支持向量机的图像边缘检测[J].山东大学学报(工学版),2006,36(3):95-99.
[76]Kundu A.Robust edge detection[J].Pattern Recognition,1990,23(5):423-440.
[77]Pal S K,King R A.On edge detection of x-ray images using fuzzy sets[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1983,5(1):69-77.
[78]Marmdani E H.Advances in the linguistic synthesis of fuzzy controller[J].International Journal of Man-Machine Studies,1976,8(6):669-679.
[79]周德龙,潘泉.图像模糊边缘检测的改进算法[J].中国图象图形学报.2001,6(4):353-358.
[80]王倩,阮海波.快速模糊边缘检测算法[J].中国图象图形学报,2001,6(1):92-95。
[81]Russo F.Edge detection in noisy images using fuzzy reasoning[J].IEEE Transactions on Instrumentation and Measurement,1998,47(5):1102-1105.
[82]陈波,杨阳,沈田双.基于模糊聚类分析的边缘检测算法[J].仪器仪表学报,2006,27(6):1603-1604.
[83]杨华千,张伟,韦鹏程,陈军.基于小波多尺度变换和模糊聚类的图像边缘检测研究[J].计算机科学,2006,33(1):274-276.
[84]Xu C.Analysis of morphological quasi-circular structure elements and skeleton [J].Acta Electronica Sinica,1999,27(8):78-81
[85]Maragos P.Tutorial on advances in morphological image processing and analysis.[J].Optical Engineering.1987,26(7):623-632
[86]Haralick R,Zhuang X.Image analysis using mathematical morphology.[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(4):532-550.
[87]Li X J.Mathematical morphological edge detectors for noisy image corrupted by impulses.[J].Journal of Image and Graphics.1998,3(11):903-906
[88]Liang Y.Application of multi-orientation morphological structure elements in edge detection.[J].J.Yunnan University,1999,21(25):392-394
[89]Todd L,Hirohisa S.Image filtering,edge detection and edge tracing using fuzzy reasoning.[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(5):481-491.
[90]Lee J,Haralick R M,Shapiro L G.Morphology Edge Detection[J].IEEE Journal of Robotics and Automation,1987,3(2):142-156.
[91]杨述斌,彭复员.噪声污染图象中的广义形态边缘检测器[J].计算机工程与应用,2002,38(17):91-92.
[92]杨述斌,彭复员,张增常.多尺度自适应加权形态边缘检测器[J].华中科技大学学报,2002,30(10):42-45.
[93]杨述斌.数学形态学在图象处理中的应用研究[D].武汉:华中科技大学,2002.
[94]冉鑫.基于可变形模型的图像分割与图像配准研究[D],上海:上海交通大学,2005.
[95]Simper A.Correcting general band-to-band misregistrations.Proceedings of the 1996 IEEE International Conference on Image Processing,Lausanne,Switz:IEEE press,1996:597-600.
[96]Irani M,Anadan P.All about direct methods[C].Proceedings of International Workshop Vision Algorithms,Corfu,Greece:IEEE press,1999:267-277.
[97]Rosenfeld A,Kak A C.Digtal Picture Processing[M].New York:Academic Press,1982.
[98]Prat W K,Digital Image Processing[M].New York:Wiley press,1991.
[99]Barnea D I,Silverman H F.A class of algorithms for fast digital image registration[J].IEEE Transactions on Computing,1972,21(2):179-186.
[100]沈庭芝,方子文.数字图像处理及模式识别[M].北京:北京理工大学出版社,1998.
[101]A.Collignon,F.Maes,D.Delaere,D.Vandermeulen,P.Suetens,G.Marchal,Automated multi-modality image registration based on information theory[C],Proceedings of the 1995 14th International Conference on Information Processing in Medical Imaging,Ile de Berder,France:[s.n.],1995,263-274.
[102]Viola P A,Wells W M,Alignment by maximization of mutual information[J].International Journal of Computer Vision,1997,24(2),137-154.
[103]Shannon C E.A mathematical theory of communication[J].Bell Systems technical Journal,27:379-423,623-656,1948.
[104]Zitova B,Flusser J.Image registration methods:a survey[J].Image and Vision Computing,2003,24:977-1000.
[105]陈昱,庄天戈.基于互信息非刚性医学图像配准的方法[J].上海交通大学学报,1999,33(9):1125-1127.
[106]罗述谦,李响.基于最大互信息的多模医学图象配准[J].中国图象图形学报,2000,5A(7):551-558.
[107]C.D.Kuglin and D.C.Hines,The phase correlation image alignment method [C].IEEE International Conference on Cybernetics and Society,New York,USA:IEEE press,1975:163-165.
[108]Alliney S.Spatial registration of multispectral and multitemporal digital imagery using fast-Fourier transform techniques[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(5),499-504.
[109]Lee D J,Krile T F,Mitra S.Digital registration techniques for sequential fundus images[C].IEEE Proc of SPIE:Applications of Digital Image Processing,New York,USA:IEEE press,1987:293-300.
[110]Reddy B S,Chatterji B N.An FFT-based technique for translation,rotation,and scale-invariant image registration[J].IEEE Transactions on Image Processing,1996,5(8),1266-1271.
[111]Mone J L.Parallel registration of multisensor remotely sensed imagery using wavelet coeficients[C].Proceeding of SPIE O/E Aerospace Sensing,Wavelet Applications,1994:432-443.
[112]Chen Q,Defrise M,Deconinck F,Symmetric phase-only matched filtering of Fourier-Mellin transform for image registration and recognition[J].IEEE Transactions on Patern Analysis and Machine Intellingence,1994,16(12):1156-1168.
[113]Bracewell R N,Chang K Y,Jha A K,Wang Y H,Affine theorem for two-dimensional Fourier transform[J].Electronics Letters,1993,29(3):304-309.
[114]刘卫光,崔江涛,周利华.插值和相位相关的图像亚像素配准方法[J].计算机辅助设计与图形学学报,2005,17(6):1273-1277.
[115]Harold S,Michael T,Orchard,Chang E C,Stephen A.Martucci,A fast direct Fourier-based algorithm for subpixel registration of images[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(10),2235-2243
[116]Shekarforoush S,Berthed M,Zerubia J.Subpixel image registration by the estimating and Pattern polyphase decomposition of cross power spectrum[C].Proceedings of the 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Francisco,CA,USA:IEEE press,1994:532-537.
[117]叶发茂.基于人工免疫系统的多源遥感图像配准研究[D].北京:中国科学院遥感应用研究所,2006.
[118]Yang Z W,Femand S.Image registration and object recognition using affine invariants and convex hulls[J].IEEE Transactions on Image Processing,1999,8(7):934-946.
[119]Chang S H,Cheng F G,Hsu W H.Fast algorithm for point pattern matching:invariant to translation,rotations and scale changes[J].Patterm Recognition,1997,30(2):311-320.
[120]Dufournaud Y,Schmid C,Horaud R.Image matching with scale adjust[J].Computer Vision and Image Understanding,2004,93(2):175-194.
[121]Koenderink J J,Van Doom A J.Representation of local geometry in the visual system[J].Biological Cybernetics,1987,55(6):367-375.
[122]仵建宁,郭宝龙,冯宗哲.一种基于兴趣点特征匹配的图像镶嵌技术[J],光电子.激光,2006,17(6):733-737.
[123]Goshtasby A.A symbolically-assisted approach to digital registration with application in computer vision[D].Michigan:Michigan State University,1983.
[124]Goshtasby A,Stockman G C,Page C V.A region-based approach to digital image registration with subpixel accuracy[J].IEEE Transactions on Geoscience and Remote Sensing,1986,GE-24(3):390-399.
[125]Ton J,Jain A K.Registering Landsat images by point matching[J].IEEE Transactions on Geoscience and Remote Sensing,1989,27(5):642-651.
[126]薛模根,韩裕生,李从利,袁广林,冷晓艳.基于区域同名度矩阵的连续帧图像配[J].中国图象图形学报,2006,11(1):53-59.
[127]刘贵喜,王蕾.基于区域选择和特征点匹配的图像配准算法[J].光电子.激光,2007,18(8):999-1002.
[128]Bouchafa S,Zavidovique B.Efficient Cumulative matching for image registration[J],Image and Computing,2006,24:70-79.
[129]张鹏强,余旭初,韩丽,刘景正.基于直线特征匹配的序列图像自动配准[J],武汉大学学报(信息科学版),2007,32(8):676-679.
[130]Dai X L,Khorram S,A Feature-Based Image Registration Algorithm Using Improved Chain-Code Representation Combined with Invariant Moments[J],IEEE Transactions on Geoscience and Remote Sensing,1999,37(5),2351-2362.
[131]Li H,Manjunath B S,Mitra S K,A Contour-Based Approach to Mutisensor Image Registration[J].IEEE Transactions on Image Processing,1995,4(3),320-333.
[132]谌安军,陈炜,毛士艺.一种基于边缘的图像配准方法[J],电子与信息学报,2004,26(5):679-684.
[133]邓鹏,王宏琦.结合边缘与灰度信息的SAR图像配准算法[J],遥感技术与应用,2003,18(3):159-164.
[134]Field D J.Relations between the statistics and natural images and the responses properties of cortical cells[J].Optical Soc.Am.,1987,A(4):2379-2394.
[135]Wainwright M J,Simoncelli E P.Scale mixtures of gaussians and the statistics of natural images[C].Proc.Neural Information Processing Systems,Cambridge,MA:MIT press,2000:855-861.
[136]Zhu S C,Mumford D B.Prior learning and gibbs reactiondiffusion[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(11):1236-1250.
[137]Bhattacharyya A.On a measure of divergence between two statistical populations defined by their probability distribution[J].Bulletin of the Calcutta Mathematical Society,1943,35:99-119.
[138]Chernoff H.A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations[J].Annals of Mathematical Statistics,1952,23(4):493-507.
[139]Bowyer K,Kranenburg C,Dougherty S.Edge detector evaluation using empirical ROC curves[J].Computer Vision and Image Understanding,2001,84(1):77-103.
[140]Liu J,Li.B,Dillon T S.An improved naive bayesian classifier technique coupled with a novel input solution method[J].IEEE Transactions on Systems,Man,and Cybernetics,2001,31(2):249-256.
[141]Henley W E,Hand D J.A k-nearest-neighbour classifier for assessing consumer credit risk[J].The Statistician,1996,45(1):77-95.
[142]Parzen E.On estimation of a probability density function and mode[J].Annals of Mathematical Statistics,1962,33(3):1065-1076.
[143]Scott K,Yuille A L,James M C,Song C Z.Statistical edge detection:learning and evaluating edge cues[J].IEEE Transactions on Pattern Analysis and Machine Intellihence,2003,25(1):57-74.
[144]Cover T M,Thomas J A.Elements of information theory[M].New York:Wiley Interscience Press,1991.
[145]Huertas A.Medioni G.Detection of intensity changes with subpixel accuracy using laplacian of Gaussian masks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(5):651.
[146]Touzi R,Lopes A,Bousquet P.A statistical an d geometrical edge detector for SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,1988,26(6):764-773.
[147]Oliver C J,Biacknell D,White R G.Optimum edge detection in SAR[J].IEEE Proceedings of Radar Sonar Navigation,1996,143(1):31-40.
[148]Benoit T,Bertrand A.A fast multi-scale edge detection algorithm[J].Pattern Recognition Letters,2004,25(6):603-618.
[149]Shafer G.A Mathematical theory of evidence[M],Princeton University Press,Princeton,1976.
[150]Bellman R,Zadeh L A.Decision making in a fuzzy environment[J].Management Science,1970,17(4):141-164.
[151]肖人彬,王雪,费奇.相关证据合成方法的研究[J].模式识别与人工智能,1993,6(3):227-234.
[152]Ulaby F T,Kouyyate F,Brisco B,Williams T.Textural information in SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,1986,24(2):235-245.
[153]Goodman J W.Some fundamental properties of speckle[J].Journal of the Optical Society of America,1976,66(11):1145-1150.
[154]Lopes A,Touzi R.Adaptive speckle filters and scene heterogencity[J].IEEE Transactions on Geoscience and Remote Sensing,1990,28(6):992-1000.
[155]Zaman M R,Moloney C R.A comparsion of adaptive filters for edge-preserving smppthing of speckle noise[C].IEEE International Conference on Acoustics,Speech and Signal Processing,Minneapolis,MN,USA:IEEE press,1993:77-80.
[156]Kenneth R C.Digital image processing[M].Beijing:Publishing House of Electronics Industry,1999.
[157]Hong Y.Unified formulation of a class of image thresholding techniques[J].Pattern Recognition,1996,29(12):2025-2032.
[158]Abdou I E,Pratt W K.Quantitative design and evaluation of enhancement/thresholding edge detectors[J].Proceedings of the IEEE,1979,67(5):753-763.
[159]Brown L G.A survey of image registration techniques[J].ACM Computing Surveys,1992,24(4):325-376.
[160]Caves R C,Harley P J,Quegan S.Matching map feature to synthetic aperture radar image using template matching[J].IEEE Transactions On Geoscience And Remote Sensing,1992,30(4):680-685.
[161]Thevenaz P,Unser M.Optimization of mutual information for multiresolution image registration[J].IEEE Transactions On Image Processing,2000,9(12):2083-2099.
[162]谌安军,陈炜,毛士艺.一种基于边缘得图像配准方法[J].电子与信息学报,2004,26(5):679-684.
[163]Medioni G G,Nevatia R.Matching images using linear features[J].IEEE Transactions on Pattern Analysis and Mechine Intelligence,1984,6(6):675-685.
[164]Kuhl P,Giardina C.Elliptic fourier features of a closed contour[J].Computer Graphics and Image Processing,1982,18(3):236-258.
[165]Dufournaud Y,Dchmid C,Horaud R.Image matching with scale adjustment[J].Computer Vision and Image Understanding,2003,93(2):175-194.
[166]Rucklidge W J.Eficiently locating objects using the hausdorf distance[J].International Journal of Computer Vision,1997,24(3):251-270.
[167]Guilloux Y.A matching algorithm for horizontal Motion,application to tracking [C].IEEE Proceeding of the 8th International Conference on Pattern Recognition,Pairs,France:IEEE Computer Society Press,1986:1190-1192.
[168]Szeliski R.Image mosaicing for tele-reality application[C].Proceedings of IEEE workshop on applications of computer,Piscataway,USA:IEEE Computer Society Press,1994:54-62.
[169]Ranade S,Rosenfeld A.Point pattern matching by relaxation[J].Pattern Recognition,1980,12(4):269-275.
[170]Choo C Y,Freeman H.An efficient technique for compressing chain-coded line drawing images[C].Signals,Systems and Computers,1992,2:717-720.
[171]陈振林.基于傅立叶描绘子的实时图像识别系统[J].仪器仪表学报,2003,24(4):391-394.
[172]Hu M K.Visual pattern recognition by moment invariants.[J].IEEE Transactions on Information Theory,1962,8(2):179-187.
[173]朱庆,吴波,万能,徐志祥,田一翔.具有良好重复率与信息量的立体影像点特征提取方法[J].电子学报,2006,34(2):205-209.
[174]Some W L.Terms of reference in data fusion[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(3):1190-1193.
[175]Pohl C,Van Genderen J.Multisensor image fusion in remote sensing:concepts,methods and applications[J].International Journal of Remote Sensing,1998,19(2):823-854.
[176]Harris C,Stephens M.A combined comer and edge detector[C].Proceedings of 4th Alvey Vision Conference,Manchester,UK:University of Manchester Press,1988:147-151.
[177]Huttenlocher D P,Klanderman G A,Rucklidge W J.Comparing images using the Hausdorff distance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(9):850-863.
[178]Borgevors G.Distance transforms in digital image[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,34(3):344-371.
[179]Dubuisson M P,Jain A K.A modified Hausdorff distance for object matching [C].Proceedings of the 12th IAPR International Conference on Pattern Recognition,Jerusalem,Isr:IEEE press,1994,566-568.
[180]Lu Y,Tan C.Chinese word searching in imaged documents[J].International Journal of Pattern Recognition and Artificial In telligence,2004,18(2):229-246.
[181]Lin K H,Lain K M,Siu W C.Spatially eigen-weighted hausdorff distances for human face recognition[J].Pattern Recognition,2003,36(8):1827-1834.
[182]Guo B F,Lam K M,Siu W C,Yang S.Human face recognition using a spatially weighted Hausdorff distance[C].IEEE International Symposium on Circuits and Systems,Sydney,NSW:Institute of Electrical and Electronics Engineers Inc,2001:145-148.
[183]Azencott R,Durbin F,Paumard J.Multiscale indentification of building in compressed aerial scenes[C].Proceedings of 13th International Conference on Pattern Recognition,Vienna,Austria:[s.n.],1996,974-978.
[184]Sire D G,Kwon O K,Park R H.Object matching algorithm using robust Hausdorff distance measures[J].IEEE Transactions on Image Processing,1999,8(3):425-429.
[185]汪亚明.图像匹配的鲁棒型Hausdorff方法[J].计算机辅助设计与图形学学报,2002,14(3):1-4.
[186]Li Y,Robert L,Stevenson,Cai J D.Comer-guided image registration by using edges[C].IEEE International Conference on Image Processing,San Antonio,Texas,USA:IEEE press,2007:361-364.
[187]黄天云,孙世新,庞朝阳.基于Gauss滤波的多媒体图像的可视化检索[J].系统工程与电子技术,2001,23(1):77-81.
[188]Koenderink JJ.The structure of images[J].Biological Cybernetics,1984,50(5):363-370.
[189]Paolo C.Optical recognition of motor vehicle license plates[J].IEEE Transactions on Vehicular Technology,1995,44(4):790-799.
[190]Wen Y,Lu Y,Shi P.Handwritten Bangla numeral recognition system and its application to postal automation[J].Pattern Recognition,2007,40(1):99-107
[191]尹朝庆,宋化,陈波.手写邮政编码的模糊识别方法[J].武汉理工大学学报(信息与管理工程版),2005,25(2):154-157.
[192]Paquet T,Lecourtier Y,Handwritten recognition:application on bank cheques [C],Proceedings of the First International Conference on Document Analysis and Recognition,Saint-Malo,France:IEEE Computer Society Press,1991:749-757.
[193]Guillevic D,Suen C y,HMM-KNN word recognition engine for bank cheque processing[C],Proceedings of the 14th International Conference on Pattern Recognition,Brisbane,Australia:IEEE Computer Society Press,1998:1526-1529.
[194]芮明,路通,苏丰,蔡士杰.基于视觉的表格自动识别方法[J].计算机应用研究,2005,22(4):256-257.
[195]刘强,晏蒲柳,郭成城,邹华.基于脊线校准的指纹点匹配算法[J].武汉大学学报(理学版),2003,49(1):102-106.
[196]俞能海,陈豪,刘政凯.基于块金字塔的快速匹配算法[J].电路与系统学 报,2003,8(2):54-57.
[197]汤泽滢,卢汉清,罗建书.基于解析形式的二维参数可变形模板匹配算法[J].中国图象图形学报,2004,9(7):775-780.
[198]朱昊,刘文耀,王金涛,郝永杰,薛白.汽车牌照的字符分割与识别[J].计算机测量与控制,2003,11(8):574-576.
[199]Chim Y C,Kassim A A,Ibrahim Y.Character recognition using statistical moments[J].Image and Vision Computing,1999,17(3):299-307.
[200]吴谨,刘洋.具有抗噪稳健性的数字识别[J].武汉科技大学学报(自然科学版),2004,27(4):406-408.
[201]Parisi R,Claudio E,Lucarell G.Car plate recognition by neural networks and image processing[J].IEEE Transaction on Neural Networks,1998,7(2):195-198.
[202]薛磊,杨晓敏,吴炜,陈默,何小海.一种基于KNN与改进SVM的车牌字符识别算法[J].四川大学学报(自然科学版),2006,43(5):1031-1036.
[203]Frigui H,Gader P,Krishnapuram R.Handwritten character membership function estimation for word recognition[J].Fuzzy Systems,2001,3(2):928-931.
[204]Turksen I B,Kandel A,Zhang Y Q.Normal forms of fuzzy middle and fuzzy contradictions[J].IEEE Transactions on Systems,Man,and Cybernetics,1999,29(2):237-253.
[205]王希雷,王磊.车牌识别中基于Rough集理论的字符识别[J].计算机工程,2006,32(24):204-205.
[206]陈继荣,黄戈.ICA和改进的SVM在有限集字符识别中的应用[J].计算机工程,2006,32(23):199-201.
[207]虞安军,吴海珍,蒋加伏.改进的遗传算法在车牌自动识别系统中的应用[J].计算机仿真,2006,23(11):224-227.
[208]蔡晋辉,张光新,周泽魁.面积重构H顶变换的实现与应用[J].光电工程,2005,32(3):73-77.
[209]张玉姣,史忠科.基于连通体检测及投影法的牌照字符切分[J].小型微型计算机系统,2004年,25(4):564-566.
[210]Hegt H A,Haye R J,Khan N A.A high performance license plate recognition system[C].IEEE International Conference on Systems,Man and Cybernetics,San Diego California,USA:IEEE Computer Society Press,1998:4357-4362.
[211]阀伟,朱秋煜.一种改进的基于模板匹配的集装箱字符识别方法[J].计算机工程,2000,26(12):119-120,143.
[212]吴炜,余艳梅,刘大宇,何小海.一种基于Hausdorff距离的车牌字符识别算法[J].计算机应用研究,2004,21(2):258-260.
[213]吴谨,邱亚.基于空间分布特征的手写体数字识别[J].武汉科技大学学报(自然科学版),2004,27(2):176-178.