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研究生:游凱倫
研究生(外文):Kai-lun Yu
論文名稱:行駛中車輛之車牌辨識分析
論文名稱(外文):Analysis of License Plate Recognition for Driving Vehicles
指導教授:李建興李建興引用關係
指導教授(外文):Chien-Hsing Lee
學位類別:碩士
校院名稱:國立成功大學
系所名稱:系統及船舶機電工程學系碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:75
中文關鍵詞:改良式主成分分析法連通標記法雷登轉換法投影量切割法歐式距離法相似符號
外文關鍵詞:projection histogram methodConnected component analysisEuclidean distanceambiguous characters.improved principal component analysisRadon transform
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本文使用Marlin F-033B攝影機拍攝車牌影像,並與電腦連結,再由Matlab/Simulink偵測車牌存在與否。當偵測到車牌時,Matlab/Simulink會自動儲存所偵測之車牌影像,再開啟所設計的圖控辨識介面,以讀取所儲存的車牌影像,進而執行車牌定位、車牌傾斜矯正、字元分割及字元辨識等步驟。首先,以連通標記法搭配車牌區域分析來偵測車牌位置,當所拍攝車牌發生傾斜時,再以雷登轉換來矯正車牌,進而以投影切割法分割車牌字元。於車牌辨識率部份,以改良式主成份分析法擷取字元特徵,再搭配歐式距離決策法,以探討搭配使用及未使用簡單分類器之車牌辨識的差異。由模擬結果得知,搭配使用與未使用簡單分類器之字元辨識成功率分別為98%與97.2%左右,而其辨識時間分別為1.3秒/張與0.8秒/張,所測試之影像皆為640×480大小。而因字元”B”和”8”、”D”和”0”、”5”和”S”、”2”和”Z”及”1”和”I”的字型相當相似,為改善相似符號難以辨別的問題,吾人利用字元邊緣線特徵的差異,作為相似符號確認的依據。
This thesis presents a dynamic real-time license plate recognition using Marlin F-033B digital camera to record an image of license plate and process with Matlab/Simulink based on GUI (graphical user interface). If the program detects a license plate in the image, the recognition system will automaticlly store the image and execute the license plate localization, license plate slant correction, character segmentation and character recognition. First, license plate in the image is localized by applying connected component analysis with the aid of license plate area analysis. If the localized license plate is slanted, the slant plate is corrected with the Radon transform and its plate character is then segmented by projection histogram method. As for license plate recognition, an improved principal component analysis is used to extract the features of the plate characters and classification of the the plate characters used the Euclidean distance method with and without the aid of using a simple classifier is investigated in the paper. As a result, the recognition rates of license plates for 640×480 pixel image are approximately to be 98% and 97.2% with and without the aid of a simple classifier, respectively. Their corresponding recognition times per license plate are about 1.3 and 0.8 seconds, respectively. Moreover, recognition of ambiguous characters on license plates such as the sets of ”B”and ”8”, ”D” and ”0”, ”5” and ”S”, ”2” and ”Z” as well as ”1” and ”I” are analyzed by using the difference between their edges to increase the recognition rate.
目 錄
頁次
誌 謝 iii
目 錄 iv
頁次 iv
表目錄 viii
圖目錄 ix
符號說明 xi
第一章 序 論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.2.1 車牌定位部份 2
1.2.2 字元分割部份 3
1.2.3 字元辨識部份 4
1.2.4 即時動態車輛即時辨識部份 6
1.3 本文所提方法與論文貢獻 6
1.4 論文架構 7
第二章 車牌辨識之硬體設備與系統流程 9
2.1 前言 9
2.2 硬體設備介紹 9
2.2.1 個人電腦 9
2.2.2 數位相機(CASIO EX-Z1200) 10
2.2.3 Marlin F-033B 1394攝影機 10
2.2.4 鏡頭 11
2.3車牌辨識系統流程 12
2.3.1靜態車牌辨識流程 13
2.3.2即時動態車輛之辨識系統流程 15
2.4 本章小結 15
第三章 車輛影像前處理 16
3.1 影像前處理 16
3.1.1 影像灰階化 16
3.1.2 直方圖等化 16
3.1.3 邊緣偵測 18
3.1.4 形態學(Morphology) 19
3.2 本章小結 22
第四章 車牌定位及字元分割 23
4.1 車牌定位 23
4.1.1 連通標記法 23
4.1.2 車牌區域分析 25
4.2傾斜車牌之矯正 25
4.2.1雷登轉換法 26
4.2.2車牌矯正 28
4.3車牌字元分割 28
4.3.1影像二值化 30
4.3.2 Otsu臨界值法 31
4.3.3適應性臨界值法 33
4.3.4車牌細部切割 34
4.3.5垂直投影量切割法 35
4.4 本章小結 36
第五章 車牌字元辨識 37
5.1 字元正規化 37
5.2字元特徵擷取 38
5.2.1 主成份分析法 38
5.2.2 改良式主成份分析法 39
5.3 決策法則 41
5.3.1 歐氏距離法 41
5.3.2 最接近特徵線 42
5.4 辨識方式比較與選擇 45
5.5 簡單分類器 46
5.6 相似符號再次確認 47
5.7 字元I與字元1的再次確認 49
5.8 本章小結 49
第六章 行駛中車輛之即時車牌辨識 51
6.1 動態車牌即時偵測 51
6.2 即時車牌辨識系統 53
6.3 本章小結 55
第七章 實驗結果 56
7.1靜態車牌辨識部份 56
7.1.1圖控介面 56
7.1.2車牌定位結果 56
7.1.3車牌矯正結果 59
7.1.4字元分割結果 59
7.1.5字元辨識結果 60
7.1.6以不同角度拍攝車輛之結果 64
7.2即時動態車牌辨識結果 65
第八章 結論與未來展望 68
8.1 結論 68
8.2 未來展望 69
參考文獻 70
簡 歷 75
參考文獻
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