基于DSP的限速标志识别系统研究
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摘要
随着汽车工业的高速发展,道路交通事故已成为世界性的社会问题。为了能有效预防交通事故的发生,提高汽车行驶的安全性,以预防为核心的汽车主动安全技术已成为现代交通的迫切要求,因此积极开展汽车主动安全技术的研究具有重要的理论意义和应用价值。
论文以汽车主动安全应用为背景,从理论和实践两个方面对基于DSP的限速标志识别技术进行了深入研究,包括基于彩色图像的限速标志检测技术、基于序贯信息的限速标志识别技术和面向DSP的移植与优化技术等。
论文完成的主要工作为:
1)通过分析中国道路限速交通标志的颜色和几何形状两种先验特征,结合Adaboost算法提出了一种基于圆覆盖的颜色滤波方法,提取出图像中的红色区域,得到二值化图像;然后采用基于梯度信息的Hough变换圆检测方法检测出图像中的圆和椭圆区域,从而完成限速交通标志的检测定位。
2)提出了一种基于序贯信息的限速标志识别算法。该算法首先采用模板匹配法对限速标志检测结果进行初步识别;然后通过引入一种基于字符拓扑特征的拒识准则对其进行判别,以决定是否拒识;最后通过相邻帧直方图巴氏距离的序贯信息,实现了视频序列图像中限速标志的识别。实验结果表明,该算法具有较好的鲁棒性和实时性。
3)实现了基于DSP的限速标志识别系统。论文选用DM642作为限速标志识别系统的硬件开发平台,在CCS软件开发环境下,针对DSP的编程特点,对基于彩色图像的限速标志检测算法和基于序贯信息的限速标志识别算法进行了移植与优化,最终实现了一个基于DSP的限速标志识别系统。
With the high development of aotumobile industry, traffic accidents have been a social problem all over the world. In order to effectively prevent the traffic accidents, and improve the driving safety, the Active Safety Technique(AST) for automobiles becomes an emergent requirement. Therefore, it has an important theoretical and practical merits to carry out the research on the AST.
Based on the research background of the AST, the thesis investigates on the traffic sign recognition from the perspective of theory and practice, including: The colorful image detection method for Speed Limit Signs (SLS), The sequential recognition method for SLS, The transplant and optimization technique oriented to DSP chips.
The main research work in this thesis contains three aspects described as follows:
Firstly, a circle covered color filtering method based on Adaboost algorithm is proposed by analyzing two types of prior properties of SLS in China. The algorithm transforms the raw image to the binary image via retrieving the red regions of the image. Next, it adopts gradient-based Hough Transformation algorithm to detect the regions of circle and ellipse for the detection and localization of SLS.
Secondly, a sequential SLS recognition algorithm is proposed. The algorithm applies template matching method to primarily recognize the SLS. Next, it decides whether to reject the sign or not based on the criterion of topologic property of the characters. Finally, the algorithm introduces the sequential information derived from histogram Bhattacharyya Distance between the neighboring frames of the vedio to recognize the SLS in video. The experimental results demonstrate that the algorithm is robust, and it can run in real-time.
Thirdly, we devolop a SLS recognition system based on the DSP chip. In the CCS software developing environment, we use the DM642 platform to transplant and optimaize both colorful images and squential vedio SLS recognition algorithms. Finally, a SLS recognition system has been implimented in the DSP-based platform.
论文以汽车主动安全应用为背景,从理论和实践两个方面对基于DSP的限速标志识别技术进行了深入研究,包括基于彩色图像的限速标志检测技术、基于序贯信息的限速标志识别技术和面向DSP的移植与优化技术等。
论文完成的主要工作为:
1)通过分析中国道路限速交通标志的颜色和几何形状两种先验特征,结合Adaboost算法提出了一种基于圆覆盖的颜色滤波方法,提取出图像中的红色区域,得到二值化图像;然后采用基于梯度信息的Hough变换圆检测方法检测出图像中的圆和椭圆区域,从而完成限速交通标志的检测定位。
2)提出了一种基于序贯信息的限速标志识别算法。该算法首先采用模板匹配法对限速标志检测结果进行初步识别;然后通过引入一种基于字符拓扑特征的拒识准则对其进行判别,以决定是否拒识;最后通过相邻帧直方图巴氏距离的序贯信息,实现了视频序列图像中限速标志的识别。实验结果表明,该算法具有较好的鲁棒性和实时性。
3)实现了基于DSP的限速标志识别系统。论文选用DM642作为限速标志识别系统的硬件开发平台,在CCS软件开发环境下,针对DSP的编程特点,对基于彩色图像的限速标志检测算法和基于序贯信息的限速标志识别算法进行了移植与优化,最终实现了一个基于DSP的限速标志识别系统。
With the high development of aotumobile industry, traffic accidents have been a social problem all over the world. In order to effectively prevent the traffic accidents, and improve the driving safety, the Active Safety Technique(AST) for automobiles becomes an emergent requirement. Therefore, it has an important theoretical and practical merits to carry out the research on the AST.
Based on the research background of the AST, the thesis investigates on the traffic sign recognition from the perspective of theory and practice, including: The colorful image detection method for Speed Limit Signs (SLS), The sequential recognition method for SLS, The transplant and optimization technique oriented to DSP chips.
The main research work in this thesis contains three aspects described as follows:
Firstly, a circle covered color filtering method based on Adaboost algorithm is proposed by analyzing two types of prior properties of SLS in China. The algorithm transforms the raw image to the binary image via retrieving the red regions of the image. Next, it adopts gradient-based Hough Transformation algorithm to detect the regions of circle and ellipse for the detection and localization of SLS.
Secondly, a sequential SLS recognition algorithm is proposed. The algorithm applies template matching method to primarily recognize the SLS. Next, it decides whether to reject the sign or not based on the criterion of topologic property of the characters. Finally, the algorithm introduces the sequential information derived from histogram Bhattacharyya Distance between the neighboring frames of the vedio to recognize the SLS in video. The experimental results demonstrate that the algorithm is robust, and it can run in real-time.
Thirdly, we devolop a SLS recognition system based on the DSP chip. In the CCS software developing environment, we use the DM642 platform to transplant and optimaize both colorful images and squential vedio SLS recognition algorithms. Finally, a SLS recognition system has been implimented in the DSP-based platform.
引文
[1] L.Paletta. Detection of Traffic Signs Using Posterior Classifier Combination[C]. IEEE, 2002:705-710.
[2] Hirose, Asakura, Aoyagi.Real-Time Recognition of Road Traffic Sign in Moving Scene Image Using New Image Filter[C]. IEEE,2000:2207-2211.
[3] A.D.L, Escalera, E.A.Moreno, M.A.Salichs, J.M.Armingol. Road traffic sign detection and classification[C]. IEEE Transactions on Industrial Electronics, 1997, 44(6):848-859.
[4] S.D.Zhu, Y.Zhang, X.F.Lu. Detection for triangle traffic sign based on neural network[C]. IEEE International Conference on Vehicular Electronics and Safety, 2005:25-28.
[5] D.M. Gavrila. Traffic Sign Recognition Revisited[J]. Proc of the 21st DAGM Symsium fur Mustererkennung, 1999:86-93.
[6] Paul Viola, Michael J. Jones. Robust Real-Time Face Detection[J]. International Journal of Computer Vision, 2004,57(2):137–154.
[7] Sin-Yu Chen, Jun-Wei Hsieh. Boosted road sign detection and recognition[C]. Machine Learning and Cybernetics International Conference, 2008:3823–3826.
[8] C. Bahlmann, Y.Zhu, Visvanathan Ramesh, M. Pellkofer, T.Koehler. A system for traffic sign detection, tracking, and recognition using color, shape, and motion information[C]. Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, June 2005:255–260.
[9] X. Baro, J Vitria. Fast traffic sign detection on greyscale images[J]. Recent Advances in Artificial Intelligence Research and Development, October 2004: 69–76.
[10] Rainer Lienhart, Jochen Maydt. An extended set of Haar-like features for rapid object detection[C].In IEEE ICIP 2002, 2002:900–903.
[11] Miguel, Ernesto. Fast Traffic Sign Detection and Recognition Under Changing Lighting Conditions[C]. Proceedings of the IEEE ITSC 2006,IEEE Intelligent Transportation Systems Conference Toronto, Canada, September, 2006:811-815.
[12] Pacheco, Joan Batlle, Xevi Cufi. A New Approach to Real Time Traffic Sign Recognition Based on Color Information[J].
[13] N.Kehtamavaz, N.C Griswold,D.S Kang. Stop-sign recognition based on color-shape processing[J]. Machine Vision and Applications, 6:206-208,1993.
[14] G. Piccioli, E. de Micheli, P.Parodia, M. Campani. Robust method for road sign detection and recognition[J]. Image and Vision Computing 1996,14(3): 209-23.
[15] Gareth Loy, Alexander Zelinsky. A fast radial symmetry transform for detecting points of interest[C]. In 7th Euproean Conference on Computer Vision,2002:358.
[16] G. Loy, N. Barnes. Fast shape-based road sign detection for a driver assistance system[C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004:70-75.
[17] A. Soetedjo, K. Yamada. Fast and robust traffic sign detection[C]. Systems, Man and Cybernetics,2005 IEEE International Conference, 2005:1341–1346.
[18] Y. Aoyagi ,T.Asakura. A study on traffic sign recognition in scene image using genetic algorithms and neural networks[C], Proc. of 22nd International Conference on Industrial Electronics, Control and Instrumentation, IEEE, 1996.
[19] A. de la Escalera, J.M. Armingol , M. Mata. Traffic sign recognition and analysis for intelligent vehicles[J], Image and Vision Computing, Vol. 21, 2003.
[20] Han Liu, Ding Liu, Jing Xin. Real-Time Recognition of Road Traffic Sign In Motion Image Based on Genetic Algorithm[C]. Pro. of the First International Conference on Machine Learning and Cybernetics, Beijing,2002:83-86.
[21] Kellmeyer D.L., Zwahlen H.T. Detection of Highway Warning Signs in Natural Video Images Using color Image Processing and Neural Networks[C]. Neural. IEEE World Congress on Computational Intelligence,1994:4226-4231.
[22] Vavilin Andrey, Kang Hyun Jo. Automatic Detection and Recognition of Traffic Signs using Geometric Structure Analysis[C]. SICE-ICASE International Joint conference, Bexco, Busan, Korea, 2006:1450-1456.
[23] Shuang dong Zhu, Lan lan Liu. Traffic Sign Recognition based on Color Standardization[C]. Proceedings of the 2006 IEEE International Conference on Information Acquisition, 2006:20– 23.
[24] Jim Torresen, Jorgen W.Bakke, Yizhuang Yang. A Camera Based Speed Limit Sign Recogniton System[C].
[25] Michael Shneier.Road Sign Detection and Recognition[C]. IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, 2005.
[26] Hasan Fleyeh. Color Detection and Segmentation for Road and Traffic Signs[C]. Proceedings of the 2004 IEEE Conference on Cybernetic and Intelligent Systems. 2004:809-815.
[27] Gege MO, Yoshinao AOKI. Recognition of Traffic Signs In Color Images[C]. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
[28] Chiung-Yao Fang, Sei-Wang Chen, Chiou-Shann Fuh. Road-Sign Detection and Tracking[C]. IEEE Transactions on Vehicular Technology, 2003 :1329-1341.
[29] Gang-Yi Jiang, Tae Young Choi. Robust Detection of Landmarks in Color Image Based on Fuzzy Set Theory[C]. Proceedings of ICSP,1998:968-971.
[30] N.Kehtarnavaz, A. Ahmad. Traffic Sign Recognition in Noisy Outdoor Scenes[J]. Department of Electrical Engineering, Texas A&M University.
[31] Gang YiJiang, Tae Young Choi, and Suk-Kyo Hong. Vision-Based Roadway Sign Recognition[J]. Transaction on Control, Automation, and Systems Engineering March, 2000:2(1).
[32] H.X.Liu, B.Ran. Vision-based stop sign detection and recognition system for intelligent vehicle[C].Transportation, Rearch Record,2001,1748:161-166.
[33] Yasser Baleghi Damavandi, Karim. Speed Limit Traffic Sign Detection & Recognition[C]. Proceedings of the 2004 IEEE Conference on Cybernetics and intelligent Systems, 2004:797-804.
[34] S.Vitabile, A.Gentile, F. Sorbello. A neural network based automatic road signs recognizer[C]. Proceedings of the 2002 International Joint Conference on Neural Networks, 2002,3:2315-2320.
[35] Júri. Detection and Recognition of Traffic Signs[D]. Disserta??o para grau de Mestre em Engenharia Electrotécnica e de Computadores,Semp,2007.
[36] Wen-Yen Wu, Tsung-Cheng Hsieh, Ching-Sung Lai. Extracting Road Signs using the Color Information[C]. Pro. of World Academy of Science, Engineering and Technology, ISSN, 2007:1307-1315.
[37] Jung-Chiehhsien,Yi-Sheng Liou, Shu-Yuan.. Road Sign Detection and Recognition Using Hidden Markov Model[J]. J.C.Hsien et al./ Asian Journal of Health and Information Sciences,2006,1(1):85-100.
[38] Yutaka Ishizuka. Segmentation of Road Sign Symbols Using Opponent-color Filters[C]. ITSWC2004, Nagoya,2004:1-8.
[39] ShuangDong, Lanlan Liu, Xiaofeng Lu. Color-Geometric Model for Traffic Sign Recognition[C]. IMACS Multiconference on "Computational Engineering in Systems Applications"(CESA), Beijing, China,2006:2028-2032.
[40] Chu Xiumin , Yan Xinping , Mao Zhe. Zhang XianzhenTraffic Sign PositioningDetection Method and Binarization in Freeway Scene Image[J]. China Journal of Highway and Transport, 2006.11.
[41] Shuangdong Zhu. The Classification of Traffic Sign Base on Fuzzy Characteristics Training Set[A].5th World Congress on Intelligent Control and Automation[C], 2004:5266-5270.
[42] Shuangdong Zhu. Two Hierarchy Classifier for Recognition of Traffic Signs Based on Neural Network[A].5th World Congress on Intelligent Control and Automation[C]. 2004:5302-5306.
[43] P.Douville. Real-time classification of traffic signs[J].Rea1-time Imaging,2000, 6(3):185-193.
[44] Cai J, Goshtasby A. Detecting human faces in color images[J]. Image and Vision Computing, 1999, 18(1):63-75.
[45] Hu Liangmei, Gao Jun, Wang Andong. A Neural Network Shape Recognition System Based on D-S Theory, Proceedings of the International Conference on Intelligent Transportation Systems, IEEE, 2003.
[46] O. Hamdoun, A. Bargeton, F.Moutarde. Detection and Recognition of the End-of- Speed-Limit and Supplementary Signs for Improved European Speed Limit Support[C]. 15th World Congress on Intelligent Transportation Systems, New York, 2008.
[47] F. Moutarde, A. Bargeton. Modular Traffic Signs Recognition Applied to On-Vehicle Real-Time Visual Detection of American and European Speed Limit Signs[C]. 15th World Congress on Intelligent Transportation Systems, New York, 2008.
[48] Hassan Shojania. Real-time Traffic Sign Detection[C].2008.
[49] Hasan, Mark. Road and Traffic Sign Detection and Recognition[J]. Advanced OR and AI Methods in Transportation.
[50] Miguel, Alberto. A First Approach to Learning a Model of Traffic Signs Using Connectionist and Syntactic Methods.
[51]王永明,尹红丽.基于Hu不变矩扩展的交通标志识别[J].曲靖师范学院学报. 2004.11,23(6):66-68.
[52]初秀民,严新平,毛喆.道路标志自动分类方法[J].交通运输工程学报,2006.10,6(4):91-94.
[53]杨斐,王坤明,马欣,朱双东.应用BP神经网络分类器识别交通标志[J].计算机工程,2003.6,29(10):120-121.
[54]杨斐.交通标志识别方法设计[J].模式识别,2006.10.
[55]郑芳平,王伟智.基于小波变换和RBF神经网络的交通标志识别[J].福洲大学学报,2007.7,35(3):397-401.
[56]王坤明,杨斐,许忠仁.基于神经网络的交通标志识别方法[J].抚顺石油学院学报,2003.3,23(1):76-80.
[57]范必双,王玉凤,王英健.基于模糊小波神经网络的交通标志识别方法研究[J].计算机仿真,2005.9,22(9):201-205.
[58]黎群辉,张航.基于改进概率神经网络的交通标志图像识别方法[J].系统工程, 2006.4,24(4):97-100.
[59]秦香英,黄亚飞,刘伟铭.基于不变矩和小波神经网络的标志识别方法[J].长沙交通学院学报,2005.6,21(2):84-88.
[60] P.Sompoch, C. Xiaoyangv, K.michiro. Automatic recognition and location of road signs from terrestrial color imagery[C]. Geoinformation&DMGIS’2001, Tailand, 2001:238-247.
[61] M.Betke, N.C.Makris. Fast Object Recognition in Noisy Images Using Simulated Annealing[C].Proceedings of the IEEE International Conference on Computer Vision USA,1995:523-530.
[62] Jun Miura, Tsuyoshi Kanda, Yoshiaki Shiral. An Active Vision System for Real-Time Traffic Sign Recognition[C]. 2000 IEEE Intelligent Transportation Systems Conference Proceedings Dearborn, USA, 2000:52-56.
[63] Yong-Jian Zheng, Werner Ritter, Reinhard Janssen. An Adaptive System For Traffic Sign Recognition. Proceedings of the Intelligent Vehicles’94 Symposium, Germany, Oct.24-26, 1994.
[64] John Hatzidimos. Automatic Traffic Sign Recognition In Digital Images[C]. Proceedings of the International Conference on Theory and Application of Mathematics and Informations, Greece,2004:174-183.
[65] Andrzej Ruta, Yongmin Li, Xiaohui Liu. Detection, Tracking and Recognition of Traffic Signs from Video Input[C].Proceedings of the 11th International IEEE, Conference on Intelligent Transportation Systems, China, 2008:55-60.
[66] Andrzej Ruta, Yongmin Li, Xiaohui Liu. Towards Real-Time Traffic Sign Recognition by Class-Specific Discriminative Features[C].Proceedings of the 11th International IEEE, Conference on Intelligent Transportation Systems, China, 2008.
[67] Syed Hassan Gilani. Road Sign Recognition based on Invariant Features usingSupport Vector Machine[D], Faculty of Electrical and Computer Engineering, The paper of Master Degree.2007.
[68] M. A. Hearst. Support Vector Machines[C], IEEE Intelligent Systems , August 1998.
[69]朱金好,罗晓萍.基于决策树型SVM的交通标志图像识别[J].长沙理工大学学报,2004.9,2(1).
[70]罗晓萍,蒋加伏,唐贤瑛.基于SVM和模糊免疫网络的交通标志图像识别[J].计算机工程与设计,2006.5, 27(9).
[71]黄志勇.基于支持向量机的交通标志识别系统的研究[D].北京工业大学,硕士学位论文,2004.5.1.
[72]刘兰兰,朱双东.两种智能交通标志分类器的比较研究[J].计算机工程与科学,2007,29(2)
[73]蒋刚毅,郑义.基于数学形态学的交通标志自动识别[J].汕头大学学报,1998 , 13(1): 90-96.
[74] J.Y.Jiang, Y.Zheng, T.Y.Choi. Morphological skeleton analysis of traffic signs on road[C]. IEEE International Conference on Systems, Man, and Cybernetics, 1996: 70-75.
[75] H. Akatsuka, S.Imai. Road sign posts recognition system[C]. Proc.SAE vehicle highway infrastructure:safety compatibility,1987:189-196.
[76] M de Saint Blancard. A study of vision-based decision making for road environment recognition[J]. In Vision-based Vehicle Guidance, Springer Series in Perception Engineering. Spinger-Verlag,1992.
[77] L. Priese, J. Klieber, R .Lakmann, V.Rehrmann, R .Schian. New results on traffic sign recognition[C]. IEEE Proc. Intelligent Vehicles'94 Symposium, 1994: 249-253.
[78] http://news.szchehang.cn/html/chexun/xinche/international/6451058706814.html.
[79] http://www.xaa.cc/F_ReadNews.asp?NewsID=10874.
[80] http://show.mercedes-benz.com.cn/2009/china/preview/e300_avantgrde.html.
[81] Luis David Lopez, Olac Fuentes. Color-Based Road Sign Detection and Tracking.Oct,2006.
[82]道路交通标志摘要[J].道路交通标志摘要(GB5768-1999).
[83]崔继文,谭久彬.基于梯度信息的随机Hough变换圆轮廓测量技术[J].红外与激光工程. 2006,35.419-423.
[84]王健,王孝通,徐晓刚,李博.基于梯度的随机Hough快速圆检测方法[J].计算机应用研究,2006,8:164-165.
[85]瞿钧,甘岚.梯度Hough变换在圆检测中的应用[J].华东交通大学学报,2007, 24(1): 101-105.
[86]刘红旗.基于Hough变换的圆检测改进算法[D].东北大学,硕士学位论文,2005.
[87]张兵权,王建峰等.一种新的对随机Hough变换改进的检测圆的方法[J].计算机工程与应用, 2006(18):53-54.
[88]王小华,谢君廷,李本伍.一种新的基于梯度方向角的圆检测算法[J].机电工程,2008,25(3):31-33.
[89]赵桂霞,黄山.一种基于随机Hough变换圆检测的改进算法[J].计算机技术与发展,2008,4(18):77-79.
[90]孙即祥著.图像分析[M].科学出版社,2004.
[91]魏宝刚,李向阳,鲁东明,潘云鹤.彩色图像分割研究进展[J].计算机科学,1999,3:59-62.
[92]张华.车牌识别系统设计与DSP实现[D].哈尔滨工程大学,硕士学位论文,2007.3.
[93]高勇.车牌识别系统中的字符分割与识别[D].安徽大学,硕士学位论文,2007.4.
[94] Rafael C. Gonzalez等著,阮秋琦等译.数字图像处理[M].电子工业出版社.2004.
[95]侯立华.图像分割方法综述[J].科技创新导报,2008,22:249.
[96]张剑,周少武,刘洁.一种基于字符分割与字符识别的LPR方法[J].计算技术与自动化,2007,26(2):111-114.
[97]陈银燕.车牌识别算法的研究与实现[D].哈尔滨理工大学,硕士学位论文,2008.3
[98]付希金.自然背景下车牌识别关键技术研究[D].东北师范大学,硕士学位论文,2008.5
[99] U.K.S. Jayarathna, G.E.M.D.C. Bandara. A Junction Based Segmentation Algorithm for Offline Handwritten Connected Character Segmentation[C]. International Conference on Computational Intelligence for Modelling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IEEE, 2006.
[100] Dejan Gorgevik, Dusan Cakmakov. An Efficient Three-Stage Classifier for Handwritten Digit Recognition[C]. Proceedings of the 17th InternationalConference on Pattern Recognition, ICPR’04, IEEE, 2004.
[101] Guorong Xuan, Xiuming Zhu, Peiqi Chai, Yun Q. Shi, Dongdong Fu. Feature Selection based on the Bhattacharyya Distance[C]. The 18th International Conference on Pattern Recognition, ICPR'06, IEEE, 2006.
[102] F.Chaker, R.Braham, F.Ghorbel. A New Method for Invariant Hand-Written Digit Recognition Using Neural Networks[C]. Image Processing and its Applications, IEEE, 1999:460-465.
[103]邢如义,夏冰,袁红云.印刷体数字快速识别方法[J].河北建筑科技学院学报,2004,23(13):65-67.
[104]贾婧,葛万成,陈康力.基于轮廓结构和统计特征的字符识别研究[J].沈阳师范大学学报(自然科学版),2006,24(1):43-46.
[105]刘建华,牛秦洲,程小辉,王勇.基于特征的印刷体数字符号识别系统[J].桂林工学院学报,2005,25(1):101-103.
[106]张凯兵.基于连通域的字符孔洞提取算法及其实现[J].西华大学学报(自然科学版),2007,26(14):53-56.
[107]王萍,刘恒,狄光敏.基于简约码特性树的字母和数字识别[J].天津大学学报,2008,41(6):667-669.
[108]王虎,冯林,孙宇哲.数字验证码识别算法的研究和设计[J].计算机工程与应用,2007, 43( 32):86-89.
[109]赵春明,石跃祥.利用投影特征高速识别车牌中的汉字[J].计算机工程与应用,2005(19):207-209.
[110]徐大宏,王润生.一种实时精确的数字识别方法[J].计算机工程与应用,2005.2:87-90.
[111]韩宏.多分类器组合及其在手写体数字识别中的应用[D].南京理工大学,博士学位论文,2000.4.
[112]董慧.手写体数字识别中的特征提取和特征选择研究[D].北京邮电大学,硕士学位论文,2007.3
[113]陈银燕,车牌识别算法的研究与实现[D].哈尔滨理工大学,硕士学位论文,2008.3.
[114]张良国,吴江琴,高文,姚鸿勋.基于Hausdorff距离的手势识别[J].中国图象图形学报, 2002, 7(11):1-5.
[115]谭锐,宣国荣.切线距离在印刷体数字识别中的应用[J].研究与设计,2002,18(12):7-9.
[116]朱秋煜,王朔中.图像特征检测和马氏距离中的数据融合与置信度[J].电子与信息学报,2008, 30(3):534-537.
[117]宫淑兰.手写数字识别的研究与应用[D].山东大学,硕士学位论文,2006.8.28
[118]刘继艳,潘建寿等.结合Kalman滤波器的Mean-Shift跟踪算法[J].计算机工程与应用,2009,45(12):184-187.
[119]赵锋,薛惠锋,王伟,吴慧.欣采用多级分类器的手写数字识别技术研究[J].微电子学与计算机,2006,23(12):31-34.
[120]刘建华,牛秦洲,程小辉,王勇.基于特征的印刷体数字符号识别系统[J].桂林工学院学报,2005,25(1):101-103.
[121]张雄伟,曹铁勇. DSP芯片的原理与开发应用(第2版)[M]. 2000.9.
[122]魏良,苏光大,邓亚峰.基于FPGA的快速入脸检测[J].电子技术应用,2006,11:33-35.
[123]北京合众达电子.SEED-VPM642用户指南[M].2005.4.
[124] Code Composer Studio Online Documentation[DB/OL].Texas Instruments, 1998.
[125] Code Composer Studio教程[M].
[126] Qianyong. DSP应用程序的移植和软件架构体系[DB/OL]. anqian88@etang.com.
[127]张志辉.基于DM642的人民币图像系统研究[D].东北大学,硕士学位论文,2006.2.1
[128]孙即祥.现代模式识别[M].高等救教育出版社. 2008.6.
[129]杨欣欣.智能移动机器人导航与控制技术的研究[D].清华大学,博士学位论文,1999,5.
[2] Hirose, Asakura, Aoyagi.Real-Time Recognition of Road Traffic Sign in Moving Scene Image Using New Image Filter[C]. IEEE,2000:2207-2211.
[3] A.D.L, Escalera, E.A.Moreno, M.A.Salichs, J.M.Armingol. Road traffic sign detection and classification[C]. IEEE Transactions on Industrial Electronics, 1997, 44(6):848-859.
[4] S.D.Zhu, Y.Zhang, X.F.Lu. Detection for triangle traffic sign based on neural network[C]. IEEE International Conference on Vehicular Electronics and Safety, 2005:25-28.
[5] D.M. Gavrila. Traffic Sign Recognition Revisited[J]. Proc of the 21st DAGM Symsium fur Mustererkennung, 1999:86-93.
[6] Paul Viola, Michael J. Jones. Robust Real-Time Face Detection[J]. International Journal of Computer Vision, 2004,57(2):137–154.
[7] Sin-Yu Chen, Jun-Wei Hsieh. Boosted road sign detection and recognition[C]. Machine Learning and Cybernetics International Conference, 2008:3823–3826.
[8] C. Bahlmann, Y.Zhu, Visvanathan Ramesh, M. Pellkofer, T.Koehler. A system for traffic sign detection, tracking, and recognition using color, shape, and motion information[C]. Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, June 2005:255–260.
[9] X. Baro, J Vitria. Fast traffic sign detection on greyscale images[J]. Recent Advances in Artificial Intelligence Research and Development, October 2004: 69–76.
[10] Rainer Lienhart, Jochen Maydt. An extended set of Haar-like features for rapid object detection[C].In IEEE ICIP 2002, 2002:900–903.
[11] Miguel, Ernesto. Fast Traffic Sign Detection and Recognition Under Changing Lighting Conditions[C]. Proceedings of the IEEE ITSC 2006,IEEE Intelligent Transportation Systems Conference Toronto, Canada, September, 2006:811-815.
[12] Pacheco, Joan Batlle, Xevi Cufi. A New Approach to Real Time Traffic Sign Recognition Based on Color Information[J].
[13] N.Kehtamavaz, N.C Griswold,D.S Kang. Stop-sign recognition based on color-shape processing[J]. Machine Vision and Applications, 6:206-208,1993.
[14] G. Piccioli, E. de Micheli, P.Parodia, M. Campani. Robust method for road sign detection and recognition[J]. Image and Vision Computing 1996,14(3): 209-23.
[15] Gareth Loy, Alexander Zelinsky. A fast radial symmetry transform for detecting points of interest[C]. In 7th Euproean Conference on Computer Vision,2002:358.
[16] G. Loy, N. Barnes. Fast shape-based road sign detection for a driver assistance system[C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004:70-75.
[17] A. Soetedjo, K. Yamada. Fast and robust traffic sign detection[C]. Systems, Man and Cybernetics,2005 IEEE International Conference, 2005:1341–1346.
[18] Y. Aoyagi ,T.Asakura. A study on traffic sign recognition in scene image using genetic algorithms and neural networks[C], Proc. of 22nd International Conference on Industrial Electronics, Control and Instrumentation, IEEE, 1996.
[19] A. de la Escalera, J.M. Armingol , M. Mata. Traffic sign recognition and analysis for intelligent vehicles[J], Image and Vision Computing, Vol. 21, 2003.
[20] Han Liu, Ding Liu, Jing Xin. Real-Time Recognition of Road Traffic Sign In Motion Image Based on Genetic Algorithm[C]. Pro. of the First International Conference on Machine Learning and Cybernetics, Beijing,2002:83-86.
[21] Kellmeyer D.L., Zwahlen H.T. Detection of Highway Warning Signs in Natural Video Images Using color Image Processing and Neural Networks[C]. Neural. IEEE World Congress on Computational Intelligence,1994:4226-4231.
[22] Vavilin Andrey, Kang Hyun Jo. Automatic Detection and Recognition of Traffic Signs using Geometric Structure Analysis[C]. SICE-ICASE International Joint conference, Bexco, Busan, Korea, 2006:1450-1456.
[23] Shuang dong Zhu, Lan lan Liu. Traffic Sign Recognition based on Color Standardization[C]. Proceedings of the 2006 IEEE International Conference on Information Acquisition, 2006:20– 23.
[24] Jim Torresen, Jorgen W.Bakke, Yizhuang Yang. A Camera Based Speed Limit Sign Recogniton System[C].
[25] Michael Shneier.Road Sign Detection and Recognition[C]. IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, 2005.
[26] Hasan Fleyeh. Color Detection and Segmentation for Road and Traffic Signs[C]. Proceedings of the 2004 IEEE Conference on Cybernetic and Intelligent Systems. 2004:809-815.
[27] Gege MO, Yoshinao AOKI. Recognition of Traffic Signs In Color Images[C]. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
[28] Chiung-Yao Fang, Sei-Wang Chen, Chiou-Shann Fuh. Road-Sign Detection and Tracking[C]. IEEE Transactions on Vehicular Technology, 2003 :1329-1341.
[29] Gang-Yi Jiang, Tae Young Choi. Robust Detection of Landmarks in Color Image Based on Fuzzy Set Theory[C]. Proceedings of ICSP,1998:968-971.
[30] N.Kehtarnavaz, A. Ahmad. Traffic Sign Recognition in Noisy Outdoor Scenes[J]. Department of Electrical Engineering, Texas A&M University.
[31] Gang YiJiang, Tae Young Choi, and Suk-Kyo Hong. Vision-Based Roadway Sign Recognition[J]. Transaction on Control, Automation, and Systems Engineering March, 2000:2(1).
[32] H.X.Liu, B.Ran. Vision-based stop sign detection and recognition system for intelligent vehicle[C].Transportation, Rearch Record,2001,1748:161-166.
[33] Yasser Baleghi Damavandi, Karim. Speed Limit Traffic Sign Detection & Recognition[C]. Proceedings of the 2004 IEEE Conference on Cybernetics and intelligent Systems, 2004:797-804.
[34] S.Vitabile, A.Gentile, F. Sorbello. A neural network based automatic road signs recognizer[C]. Proceedings of the 2002 International Joint Conference on Neural Networks, 2002,3:2315-2320.
[35] Júri. Detection and Recognition of Traffic Signs[D]. Disserta??o para grau de Mestre em Engenharia Electrotécnica e de Computadores,Semp,2007.
[36] Wen-Yen Wu, Tsung-Cheng Hsieh, Ching-Sung Lai. Extracting Road Signs using the Color Information[C]. Pro. of World Academy of Science, Engineering and Technology, ISSN, 2007:1307-1315.
[37] Jung-Chiehhsien,Yi-Sheng Liou, Shu-Yuan.. Road Sign Detection and Recognition Using Hidden Markov Model[J]. J.C.Hsien et al./ Asian Journal of Health and Information Sciences,2006,1(1):85-100.
[38] Yutaka Ishizuka. Segmentation of Road Sign Symbols Using Opponent-color Filters[C]. ITSWC2004, Nagoya,2004:1-8.
[39] ShuangDong, Lanlan Liu, Xiaofeng Lu. Color-Geometric Model for Traffic Sign Recognition[C]. IMACS Multiconference on "Computational Engineering in Systems Applications"(CESA), Beijing, China,2006:2028-2032.
[40] Chu Xiumin , Yan Xinping , Mao Zhe. Zhang XianzhenTraffic Sign PositioningDetection Method and Binarization in Freeway Scene Image[J]. China Journal of Highway and Transport, 2006.11.
[41] Shuangdong Zhu. The Classification of Traffic Sign Base on Fuzzy Characteristics Training Set[A].5th World Congress on Intelligent Control and Automation[C], 2004:5266-5270.
[42] Shuangdong Zhu. Two Hierarchy Classifier for Recognition of Traffic Signs Based on Neural Network[A].5th World Congress on Intelligent Control and Automation[C]. 2004:5302-5306.
[43] P.Douville. Real-time classification of traffic signs[J].Rea1-time Imaging,2000, 6(3):185-193.
[44] Cai J, Goshtasby A. Detecting human faces in color images[J]. Image and Vision Computing, 1999, 18(1):63-75.
[45] Hu Liangmei, Gao Jun, Wang Andong. A Neural Network Shape Recognition System Based on D-S Theory, Proceedings of the International Conference on Intelligent Transportation Systems, IEEE, 2003.
[46] O. Hamdoun, A. Bargeton, F.Moutarde. Detection and Recognition of the End-of- Speed-Limit and Supplementary Signs for Improved European Speed Limit Support[C]. 15th World Congress on Intelligent Transportation Systems, New York, 2008.
[47] F. Moutarde, A. Bargeton. Modular Traffic Signs Recognition Applied to On-Vehicle Real-Time Visual Detection of American and European Speed Limit Signs[C]. 15th World Congress on Intelligent Transportation Systems, New York, 2008.
[48] Hassan Shojania. Real-time Traffic Sign Detection[C].2008.
[49] Hasan, Mark. Road and Traffic Sign Detection and Recognition[J]. Advanced OR and AI Methods in Transportation.
[50] Miguel, Alberto. A First Approach to Learning a Model of Traffic Signs Using Connectionist and Syntactic Methods.
[51]王永明,尹红丽.基于Hu不变矩扩展的交通标志识别[J].曲靖师范学院学报. 2004.11,23(6):66-68.
[52]初秀民,严新平,毛喆.道路标志自动分类方法[J].交通运输工程学报,2006.10,6(4):91-94.
[53]杨斐,王坤明,马欣,朱双东.应用BP神经网络分类器识别交通标志[J].计算机工程,2003.6,29(10):120-121.
[54]杨斐.交通标志识别方法设计[J].模式识别,2006.10.
[55]郑芳平,王伟智.基于小波变换和RBF神经网络的交通标志识别[J].福洲大学学报,2007.7,35(3):397-401.
[56]王坤明,杨斐,许忠仁.基于神经网络的交通标志识别方法[J].抚顺石油学院学报,2003.3,23(1):76-80.
[57]范必双,王玉凤,王英健.基于模糊小波神经网络的交通标志识别方法研究[J].计算机仿真,2005.9,22(9):201-205.
[58]黎群辉,张航.基于改进概率神经网络的交通标志图像识别方法[J].系统工程, 2006.4,24(4):97-100.
[59]秦香英,黄亚飞,刘伟铭.基于不变矩和小波神经网络的标志识别方法[J].长沙交通学院学报,2005.6,21(2):84-88.
[60] P.Sompoch, C. Xiaoyangv, K.michiro. Automatic recognition and location of road signs from terrestrial color imagery[C]. Geoinformation&DMGIS’2001, Tailand, 2001:238-247.
[61] M.Betke, N.C.Makris. Fast Object Recognition in Noisy Images Using Simulated Annealing[C].Proceedings of the IEEE International Conference on Computer Vision USA,1995:523-530.
[62] Jun Miura, Tsuyoshi Kanda, Yoshiaki Shiral. An Active Vision System for Real-Time Traffic Sign Recognition[C]. 2000 IEEE Intelligent Transportation Systems Conference Proceedings Dearborn, USA, 2000:52-56.
[63] Yong-Jian Zheng, Werner Ritter, Reinhard Janssen. An Adaptive System For Traffic Sign Recognition. Proceedings of the Intelligent Vehicles’94 Symposium, Germany, Oct.24-26, 1994.
[64] John Hatzidimos. Automatic Traffic Sign Recognition In Digital Images[C]. Proceedings of the International Conference on Theory and Application of Mathematics and Informations, Greece,2004:174-183.
[65] Andrzej Ruta, Yongmin Li, Xiaohui Liu. Detection, Tracking and Recognition of Traffic Signs from Video Input[C].Proceedings of the 11th International IEEE, Conference on Intelligent Transportation Systems, China, 2008:55-60.
[66] Andrzej Ruta, Yongmin Li, Xiaohui Liu. Towards Real-Time Traffic Sign Recognition by Class-Specific Discriminative Features[C].Proceedings of the 11th International IEEE, Conference on Intelligent Transportation Systems, China, 2008.
[67] Syed Hassan Gilani. Road Sign Recognition based on Invariant Features usingSupport Vector Machine[D], Faculty of Electrical and Computer Engineering, The paper of Master Degree.2007.
[68] M. A. Hearst. Support Vector Machines[C], IEEE Intelligent Systems , August 1998.
[69]朱金好,罗晓萍.基于决策树型SVM的交通标志图像识别[J].长沙理工大学学报,2004.9,2(1).
[70]罗晓萍,蒋加伏,唐贤瑛.基于SVM和模糊免疫网络的交通标志图像识别[J].计算机工程与设计,2006.5, 27(9).
[71]黄志勇.基于支持向量机的交通标志识别系统的研究[D].北京工业大学,硕士学位论文,2004.5.1.
[72]刘兰兰,朱双东.两种智能交通标志分类器的比较研究[J].计算机工程与科学,2007,29(2)
[73]蒋刚毅,郑义.基于数学形态学的交通标志自动识别[J].汕头大学学报,1998 , 13(1): 90-96.
[74] J.Y.Jiang, Y.Zheng, T.Y.Choi. Morphological skeleton analysis of traffic signs on road[C]. IEEE International Conference on Systems, Man, and Cybernetics, 1996: 70-75.
[75] H. Akatsuka, S.Imai. Road sign posts recognition system[C]. Proc.SAE vehicle highway infrastructure:safety compatibility,1987:189-196.
[76] M de Saint Blancard. A study of vision-based decision making for road environment recognition[J]. In Vision-based Vehicle Guidance, Springer Series in Perception Engineering. Spinger-Verlag,1992.
[77] L. Priese, J. Klieber, R .Lakmann, V.Rehrmann, R .Schian. New results on traffic sign recognition[C]. IEEE Proc. Intelligent Vehicles'94 Symposium, 1994: 249-253.
[78] http://news.szchehang.cn/html/chexun/xinche/international/6451058706814.html.
[79] http://www.xaa.cc/F_ReadNews.asp?NewsID=10874.
[80] http://show.mercedes-benz.com.cn/2009/china/preview/e300_avantgrde.html.
[81] Luis David Lopez, Olac Fuentes. Color-Based Road Sign Detection and Tracking.Oct,2006.
[82]道路交通标志摘要[J].道路交通标志摘要(GB5768-1999).
[83]崔继文,谭久彬.基于梯度信息的随机Hough变换圆轮廓测量技术[J].红外与激光工程. 2006,35.419-423.
[84]王健,王孝通,徐晓刚,李博.基于梯度的随机Hough快速圆检测方法[J].计算机应用研究,2006,8:164-165.
[85]瞿钧,甘岚.梯度Hough变换在圆检测中的应用[J].华东交通大学学报,2007, 24(1): 101-105.
[86]刘红旗.基于Hough变换的圆检测改进算法[D].东北大学,硕士学位论文,2005.
[87]张兵权,王建峰等.一种新的对随机Hough变换改进的检测圆的方法[J].计算机工程与应用, 2006(18):53-54.
[88]王小华,谢君廷,李本伍.一种新的基于梯度方向角的圆检测算法[J].机电工程,2008,25(3):31-33.
[89]赵桂霞,黄山.一种基于随机Hough变换圆检测的改进算法[J].计算机技术与发展,2008,4(18):77-79.
[90]孙即祥著.图像分析[M].科学出版社,2004.
[91]魏宝刚,李向阳,鲁东明,潘云鹤.彩色图像分割研究进展[J].计算机科学,1999,3:59-62.
[92]张华.车牌识别系统设计与DSP实现[D].哈尔滨工程大学,硕士学位论文,2007.3.
[93]高勇.车牌识别系统中的字符分割与识别[D].安徽大学,硕士学位论文,2007.4.
[94] Rafael C. Gonzalez等著,阮秋琦等译.数字图像处理[M].电子工业出版社.2004.
[95]侯立华.图像分割方法综述[J].科技创新导报,2008,22:249.
[96]张剑,周少武,刘洁.一种基于字符分割与字符识别的LPR方法[J].计算技术与自动化,2007,26(2):111-114.
[97]陈银燕.车牌识别算法的研究与实现[D].哈尔滨理工大学,硕士学位论文,2008.3
[98]付希金.自然背景下车牌识别关键技术研究[D].东北师范大学,硕士学位论文,2008.5
[99] U.K.S. Jayarathna, G.E.M.D.C. Bandara. A Junction Based Segmentation Algorithm for Offline Handwritten Connected Character Segmentation[C]. International Conference on Computational Intelligence for Modelling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IEEE, 2006.
[100] Dejan Gorgevik, Dusan Cakmakov. An Efficient Three-Stage Classifier for Handwritten Digit Recognition[C]. Proceedings of the 17th InternationalConference on Pattern Recognition, ICPR’04, IEEE, 2004.
[101] Guorong Xuan, Xiuming Zhu, Peiqi Chai, Yun Q. Shi, Dongdong Fu. Feature Selection based on the Bhattacharyya Distance[C]. The 18th International Conference on Pattern Recognition, ICPR'06, IEEE, 2006.
[102] F.Chaker, R.Braham, F.Ghorbel. A New Method for Invariant Hand-Written Digit Recognition Using Neural Networks[C]. Image Processing and its Applications, IEEE, 1999:460-465.
[103]邢如义,夏冰,袁红云.印刷体数字快速识别方法[J].河北建筑科技学院学报,2004,23(13):65-67.
[104]贾婧,葛万成,陈康力.基于轮廓结构和统计特征的字符识别研究[J].沈阳师范大学学报(自然科学版),2006,24(1):43-46.
[105]刘建华,牛秦洲,程小辉,王勇.基于特征的印刷体数字符号识别系统[J].桂林工学院学报,2005,25(1):101-103.
[106]张凯兵.基于连通域的字符孔洞提取算法及其实现[J].西华大学学报(自然科学版),2007,26(14):53-56.
[107]王萍,刘恒,狄光敏.基于简约码特性树的字母和数字识别[J].天津大学学报,2008,41(6):667-669.
[108]王虎,冯林,孙宇哲.数字验证码识别算法的研究和设计[J].计算机工程与应用,2007, 43( 32):86-89.
[109]赵春明,石跃祥.利用投影特征高速识别车牌中的汉字[J].计算机工程与应用,2005(19):207-209.
[110]徐大宏,王润生.一种实时精确的数字识别方法[J].计算机工程与应用,2005.2:87-90.
[111]韩宏.多分类器组合及其在手写体数字识别中的应用[D].南京理工大学,博士学位论文,2000.4.
[112]董慧.手写体数字识别中的特征提取和特征选择研究[D].北京邮电大学,硕士学位论文,2007.3
[113]陈银燕,车牌识别算法的研究与实现[D].哈尔滨理工大学,硕士学位论文,2008.3.
[114]张良国,吴江琴,高文,姚鸿勋.基于Hausdorff距离的手势识别[J].中国图象图形学报, 2002, 7(11):1-5.
[115]谭锐,宣国荣.切线距离在印刷体数字识别中的应用[J].研究与设计,2002,18(12):7-9.
[116]朱秋煜,王朔中.图像特征检测和马氏距离中的数据融合与置信度[J].电子与信息学报,2008, 30(3):534-537.
[117]宫淑兰.手写数字识别的研究与应用[D].山东大学,硕士学位论文,2006.8.28
[118]刘继艳,潘建寿等.结合Kalman滤波器的Mean-Shift跟踪算法[J].计算机工程与应用,2009,45(12):184-187.
[119]赵锋,薛惠锋,王伟,吴慧.欣采用多级分类器的手写数字识别技术研究[J].微电子学与计算机,2006,23(12):31-34.
[120]刘建华,牛秦洲,程小辉,王勇.基于特征的印刷体数字符号识别系统[J].桂林工学院学报,2005,25(1):101-103.
[121]张雄伟,曹铁勇. DSP芯片的原理与开发应用(第2版)[M]. 2000.9.
[122]魏良,苏光大,邓亚峰.基于FPGA的快速入脸检测[J].电子技术应用,2006,11:33-35.
[123]北京合众达电子.SEED-VPM642用户指南[M].2005.4.
[124] Code Composer Studio Online Documentation[DB/OL].Texas Instruments, 1998.
[125] Code Composer Studio教程[M].
[126] Qianyong. DSP应用程序的移植和软件架构体系[DB/OL]. anqian88@etang.com.
[127]张志辉.基于DM642的人民币图像系统研究[D].东北大学,硕士学位论文,2006.2.1
[128]孙即祥.现代模式识别[M].高等救教育出版社. 2008.6.
[129]杨欣欣.智能移动机器人导航与控制技术的研究[D].清华大学,博士学位论文,1999,5.