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研究生:蘇靖淵
研究生(外文):Ching-Yuan Su
論文名稱:智慧型手機之陀螺儀傳感器車牌辨識系統
論文名稱(外文):License Plate Recognition System with Gyroscope Sensor in Smart Phone
指導教授:王圳木王圳木引用關係林耿呈
指導教授(外文):Chuin-Mu WangGeng-Cheng Lin
學位類別:碩士
校院名稱:國立勤益科技大學
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:2013
畢業學年度:101
語文別:中文
論文頁數:102
中文關鍵詞:隨機森林字元辨識歪斜車牌陀螺儀傳感器Android
外文關鍵詞:Random ForestCharacter RecognitionTiltLicense PlateGyroscope SensorAndroid
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本篇論文提出了建置車牌辨識系統於智慧型手機上,並使用隨機森林法(Random Forest, RF)概念的變形加以應用,作為辨識車牌字元的基礎;為了使智慧型手機上的系統快速又準確,我們設計了相對應的工作流程進行參數的選取,並透過個人電腦完成;使用手機車牌辨識系統時,內建的攝影鏡頭會回傳連續的靜態影像,然而,手持式裝置擷取到的影像,會因使用者本身造成些微,甚至劇烈的晃動,而直接影響某些演算法的效能,而本篇論文提出之車牌辨識系統演算法,搭配手機內建之陀螺儀傳感器(Gyroscope Sensor, G-Sensor)所回傳的角度來對影像作旋轉校正,藉此讓影像具有旋轉不變性,解決了此問題。
系統演算法主要包含了影像前處理、車牌位置偵測與擷取、車牌區域元件切割、車牌區域元件特徵萃取,以及車牌元件辨識等幾個部份。影像前處理的主要目的,是為了能將影像資訊簡單化,一方面能使系統更快速,一方面也能使找尋車牌的位置時更為準確,我們在系統中設置「感興趣區域」與「有效偵測角度」,令使用者以較直覺的方式使用此系統,並於後續步驟能快速地獲得結果。在找尋車牌時,計數影像中每一條水平線上的顏色變化次數,透過門檻篩選出符合條件的區域,即可找出車牌位置,而初次定位結果之區域大小,將作為偵測異常車牌的判斷依據,進行條件式迭代二值化、修正,並重新搜尋,藉此取得更好的車牌位置,也提升了車牌元件的切割準確率。在車牌元件切割部份,我們利用目標元件與背景之對比特性,偵測顏色變化,找出元件邊界並切割。最後的辨識階段,由於切割得到的元件包含了車牌字元與非車牌字元,我們的目標是分類出該元件是屬於何種字元,並同時分辨出該元件屬於字元或非字元;從實驗結果可以得知,本篇論文所提出之演算方式,具有非常好的車牌元件辨識能力。

In this thesis, we propose a method and implement a license plate recognition system, LPRS, on a smart-phone. The recognition algorithm of LPRS is based on a similar random forest method. We plan several tasks and finish them by computer in order to make a thin, fast, and accurate system. There are static and continuous images when the LPRS is detecting by camera of smart-phone. However, the images from handheld device are influenced because the user has tremor. That would be effect directly for choosing suitable algorithm, but the proposed method is solved this problem by using return tri-axial information from gyroscope sensor of smart phone. Therefore, the images from camera capture are provided with rotation-invariant.
The stages of LPRS are included image preprocessing, license plate detection and capture (LPDC), license plate area elements segmentation (LPAES), license plate area elements features computing (LPAEFC), and license plate elements recognition (LPER). The main goal of image preprocessing stage is reducing information of image to make them simply. Let the system become more efficient, and on the other, it will get location of license plate more accurate. We set up a region of interest (ROI) and an effective range of angle of detection. According to the setting, user can use LPRS intuitively and get result quickly. After setting and image preprocessing, it is scanned ROI and found the location of license plate through setting thresholds, upper and lower limit of color changing. However, the result depends. There is a rectangle for circling location of license plate. Sometime the rectangle size is too strange. The reason is that the LPRS is got a bad image of binarization, we can use it to check that is a wrong result or not. We call this processing conditional binarization. Then, the location of license plate will be re-found and got right result. If this processing is working, the LPAES and LPER stage will be improved. At the LPAES stage, we use the contrast between detecting element and background and cut them at their edges. The final stage, recognition, our goal is not only classify characters, but also classify text and non-text (noisy). According to experimental results, the propose method and system are provided with a very good recognition ability.
論文口試委員會審定書 i
中文摘要 ii
ABSTRACT iv
誌謝 vi
目錄 vii
表目錄 x
圖目錄 xi
第一章、緒論 1
1.1 研究背景 1
1.2 相關研究探討 4
1.3 台灣車牌種類 6
1.4 論文架構 10
第二章、系統建置工作與流程 11
2.1 簡介 11
2.2 系統建置工作 12
2.3 車牌辨識系統流程 14
2.4 特徵表建立流程 17
2.5 特徵參數選取流程 20
2.5.1 特徵位元數量 20
2.5.2 特徵表數量 23
2.5.3 特徵值萃取位置 25
第三章、車牌辨識系統 27
3.1 影像前處理 27
3.1.1 簡介 27
3.1.2 感興趣區域設置 29
3.1.3 G-Sensor角度擷取與修正 31
3.1.4 ROI前處理 35
3.2 車牌位置偵測與擷取 39
3.2.1 水平變化量標記法 39
3.2.2 條件式迭代二值化 44
3.3 車牌區域元件切割 48
3.4 車牌元件辨識 49
3.4.1 特徵萃取 49
3.4.2 元件辨識 51
第四章、基於隨機森林法之元件特徵表 53
4.1 簡介 53
4.2 隨機森林法原理 54
4.2.1 決策樹 54
4.2.2 建立隨機森林 58
4.3 樣板資料庫 61
4.4 特徵空間轉換 65
4.5 特徵參數選取 67
4.5.1 特徵位元數量 67
4.5.2 特徵表數量 71
4.5.3 特徵值萃取位置 75
第五章、實驗結果 77
5.1 特徵表辨識率 77
5.2 系統測試 78
第六章、結論 83
參考文獻 84

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