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研究生:賴建庭
研究生(外文):Chien-Ting Lai
論文名稱:混合專家模型應用於影像車號辨識
論文名稱(外文):Application of Mixture of Experts Model to Vehicle License Plates Recognition
指導教授:張財榮張財榮引用關係
指導教授(外文):Tsai-Rong Chang
學位類別:碩士
校院名稱:南台科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:57
中文關鍵詞:混合專家模型車牌辨識委員會機器自組織映射網路
外文關鍵詞:Mixture of ExpertsVehicle License Plate RecognitionCommittee MachineSelf-Organization Map
相關次數:
  • 被引用被引用:13
  • 點閱點閱:365
  • 評分評分:
  • 下載下載:71
  • 收藏至我的研究室書目清單書目收藏:0
本研究將車牌辨識流程分為四大部分,分別為前處理、車牌區域定位、字元分割以及字元辨識,並針對各個部分常見的問題提出改善方法。其中車牌區域定位是基於模糊理論方法來完成,在進行影像的飽和度、強度與邊緣等分量擷取之後,利用飽和度、強度歸屬函數以及邊緣歸屬函數計算影像中每個位置屬於車牌的程度,使得車牌區域在歸屬影像中更加突顯,之後再整合模糊集合,利用均值濾波器將影像中歸屬值密度較高的區域集結在一起,並從中取出候選區域,再對候選區域加以評分以得到精確的車牌區域。在字元分割部分,則以連通元件為基礎,先以頂點間連線角度找出車牌傾斜角度以進行校正,並利用元件的高度、座標特徵進行歸類以篩選車牌字元,透過此方法,以極少的計算量即可達到有效的傾斜偵測與字元篩選。而字元辨識部分,本研究提出以混合專家模型來建構辨識系統,此模型是基於委員會機器為概念所發展出的架構,其中自組織映射網路扮演了類別分派器以及閘道網路的角色,分派器將輸入字元分派到若干個獨立的專家子網路,而閘道網路則在辨識過程中決定各個子網路的影響權重,此方法可以降低每個專家所需辨識的字元個數,專注於屬性相近的樣本辨識,進而達到提高辨識率的效果,由實驗結果可知本系統可達百分之九十七的車牌辨識率以及百分之九十九點四的字元辨識率。
In this study, the complete procedure of image-based license plate number recognition was presented, and we addressed the improvement for the common problems in each parts of license plate recognition system. The whole procedure was divided into four sections, that were separately preprocessing, vehicle plate area locating, character segmentation and character recognition. The vehicle plate area locating was achieved by the method base on fuzzy theory. The degree of pixels belonging amount vehicle plate were measured by two membership functions which have input parameters that respectively were saturation and intensity for the one and the edge feature for another. After fuzzy computation, used the two dimensional average filtering to get the areas which had higher density membership value and took them as the candidates for evaluating the exact license plate location. In the section of character segmentation, the fast, effective clustering algorithm and the tilt corrective algorithm were used for getting the correct and modified character connective components. In order to make the performance of character recognition higher, the module of mixture of experts was adopted for character recognition. The self-organization map acted the assigner and the gating network in this module, assigned similar classes into each sub-network called expert in the constructional period; Then, decided the decision weight of each expert in the active period. Thus, the amount of classes which each expert in charged would have been reduced so that the recognition rate will be promoted. After the experiment, we found that has obtained a 97.0% recognition rate of license plates and a 99.4% recognition rate of characters.
摘要 iv
致謝 vi
目次 vii
表目錄 ix
圖目錄 x
第一章 緒論 1
1-1 研究動機與背景 1
1-2 研究條件限制 4
1-3 章節架構 5
第二章 相關背景知識與文獻 6
2-1 相關背景知識 6
2-1-1 動態二值化門檻值選取 6
2-1-2 連通元件標示法 8
2-1-3 HSI色彩空間 10
2-1-4 倒傳遞類神經網路 12
2-2 相關文獻 14
2-2-1 車牌區域定位 14
2-2-2 傾斜偵測 16
2-2-3 字元擷取 18
2-2-4 辨識系統 19
第三章 系統架構 22
3-1 系統概觀 22
3-2 車牌定位 23
3-2-1 強度與飽和度歸屬函數 24
3-2-2 邊緣歸屬函數 26
3-2-3 候選區域提取 28
3-2-4 候選區域評分 29
3-2-5 邊界搜尋 30
3-3 字元擷取 32
3-3-1 傾斜偵測與校正 33
3-3-2 元件篩選 35
3-3-3 字元補償 36
3-4 字元辨識 37
3-4-1 混合專家模型 38
3-4-2 自組織映射網路 39
3-4-3 辨識系統的組態 41
第四章 研究成果 45
4-1 實驗與討論 45
4-1-1 第一階段實驗 45
4-1-2 第二階段實驗 47
4-2 開發工作 51
第五章 結論 53
參考文獻 55
[1] 交通部運輸研究所,「台灣地區智慧型運輸系統綱要計畫(2004年版)」,民國九十三年。
[2] 交通部公路總局,「汽機車牌照資料」。
[3] 莊志鴻,林欣萍,「車牌辨識系統之方法」,中華民國專利公報,第三十四卷第六期,民國九十年。
[4] 林泰良,「智慧型車牌定位與字串分割」,國立台灣大學,電機工程學研究所碩士論文,民國八十九年。
[5] 張仁豪,「正交軸投影法與樹狀決策在汽車牌照辨識的研究」,國立清華大學,原子科學系碩士論文,民國九十年。
[6] 蔡銘鑫,「小波轉換和類神經網路應用於車牌辨識」,朝陽科技大學,資訊工程系碩士論文,民國九十三年。
[7] 張斐章,張麗秋,「類神經網路」,東華書局,民國九十四年。
[8] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998.
[9] R. C. Gonzalez, R. E. Woods, Digital Image Processing (2nd), Prentice Hall, 2002.
[10] R. Zunino, S. Rovetta, “Vector quantization for license-plate location and image coding,” IEEE Trans. on Industrial Electronics, vol. 47, pp. 159-167, 2000.
[11] S.H. Park, K.I Kim, K. Jung, H.J. Kim, “Locating car license plates using neural networks,” IEE Electronics Letters, vol. 35, pp.1475-1477, 1999.
[12] J.F. Xu, S.F. Li , M.S. Yu, “Car license plate extraction using color and edge information,” Int. Conf. on Machine Learning and Cybernetics, vol. 6, pp. 3904-3907, 2004.
[13] T. Kohonen, “The self-organizing map,” IEEE Proc. Digital Object Identifier, vol. 78, pp. 1464-1480, 1990.
[14] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. on System, Man, and, Cyber-netics, Vol. SMC-
9, pp. 62-66, 1979.
[15] S.L. Chang; L.S. Chen, Y.C. Chung, S.W. Chen, “Automatic license plate recognition,” IEEE Trans. on Intelligent Transportation Systems, vol. 5, pp. 42-53, 2004.
[16] C.T. Hsieh, Y.S. Juan, K.M. Hung, “Multiple license plate detection for complex background,” Int. Conf. on Advanced Information Networking and Applications, vol. 2, pp. 389-392, 2005.
[17] Y. Cheng, J. Lu, T. Yahagi, “Car license plate recognition based on the combination of principal components analysis and radial basis function networks,” Proc. ISCP Int. Conf on Signal Processing, vol. 2, pp. 1455-1458, 2004.
[18] G. He Ming, A.L. Harvey, T. Vinay, “Hough Transform In Car Number Plate Skew Detection,” Int. Symposium on Signal Processing and Its Applications, vol. 2, pp. 593-596, 1996.
[19] M. Chen, X. Ding, “A robust skew detection algorithm for grayscale document image,” Int. Conf. on Document Analysis and Recognition, pp. 617-620, 1999.
[20] J.A.G. Nijhuis, M.H. Ter Brugge, K.A. Helmholt, J.P.W. Pluim, L. Spaanenburg, R.S. Venema, M.A. Westenberg, “Car license plate recognition with neural networks and fuzzy logic,” IEEE Int. Conf. on Neural Networks, vol. 5, pp. 2232-2236, 1995.
[21] N. Zimic, J. Ficzko, M, Mraz, J. Virant, “The fuzzy logic approach to the car number plate locating problem,” Proc. Intelligent Information Systems, pp.227-230, 1997.
[22] P. Comelli, P. Ferragina, M.N. Granieri, F. Stabile, “Optical recognition of motor vehicle license plates,” IEEE Trans. on Vehicular Technology, vol. 44, pp. 790-799, 1995.
[23] W. Cheokman, C.O. Lei, H.W. Chan, S.K. Tong, N. Kengchung, “A Macao license plate recognition system,” Int. Conf. on Machine Learning and Cybernetics, vol. 7, pp.4506-4510, 2005.
[24] Y.P. Huang, S.Y. Lai, W.P. Chuang, “A template-based model for license plate recognition,” IEEE Int. Conf. on Networking, Sensing and Control, vol. 2, pp. 737-742, 2004.
[25] H.A. Hegt, R.J. de la Haye, N.A. Khan, “A high performance license plate recognition system,” IEEE Int. Conf. on Systems, Man, and Cybernetics, vol. 5, pp. 4357-4362, 1998.
[26] C.N.E. Anagnostopoulos, I.E. Anagnostopoulos, V. Loumos, E. Kayafas, “A License Plate-Recognition Algorithm for Intelligent Transportation System Applications,” IEEE Trans. on Intelligent Transportation Systems, vol. 7, pp. 377-392, 2006.
[27] D. Pruegsa; T. Arit, “Thai Vehicle License Plate Recognition Using the Hierarchical Cross-correlation ARTMAP,” IEEE Int. Conf. on Intelligent Systems 2006 3rd, pp. 652-655, 2006.
[28] S.W. Chen; G.C. Stockman, K.E. Chang, “Corrections to “SO Dynamic Deformation for Building of 3-D Models”,” IEEE Trans. on Neural Networks, vol. 7, pp. 1314, 1996.
[29] P. Blythe, “RFID for road tolling, road-use pricing and vehicle access control,” IEE Colloquium on RFID Technology, pp. 811-816, 1999.
[30] J. Qiu, B. Sun, Q. You, “Study on RFID Antenna for Railway Vehicle Identification,” Int. Conf. on ITS Telecommunications Proceedings, pp. 237-240, 2006.
[31] I.R. Urazghildiiev, R. Ragnarsson, K. Wallin, A. Rydberg, P. Ridderstrom, E. Ojefors, “A vehicle classification system based on microwave radar measurement of height profiles,” RADAR 2002, pp. 409-413, 2002.
[32] T. Yamaguchi, Y. Nakano, M. Maruyama, H. Miyao, T. Hananoi, “Digit classification on signboards for telephone number recognition,” Int. Conf. on Document Analysis and Recognition, pp. 359-363, 2003.
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