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研究生: 陳勉光
Chen mian guang
論文名稱: 利用可攜式鏡頭輔助視障者即時辨識公車車號
Helping the Blind to Identify City Bus Numbers with the Portable Digital Camera
指導教授: 葉榮木
Yeh, Zong-Mu
蔡俊明
Tsai, Chun-Ming
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2010
畢業學年度: 98
語文別: 中文
論文頁數: 78
中文關鍵詞: 區域分割相鄰相減前景擷取字元辨識語音播放系統
英文關鍵詞: Segmentation, Frame difference, Foreground Extraction, OCR, MS SAPI 5.1
論文種類: 學術論文
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  • 視障者搭乘公車時面臨許多困難,其中無法辨識車號是最關鍵的問題。目前解決此問題的方法是請求路人協助,或手持自製車號牌引起公車駕駛注意,但上述方法皆屬被動性,可變因素較大。有鑑於數位影像處理技術的日漸成熟及攝影機硬體成本的降低,本研究基於數位影像處理技術,利用數位相機的鏡頭模擬,輔助視障者即時辨識公車車號,並以其他感官方式發出提示訊息。本研究以主動搜尋、辨識為目標,並提升系統執行速度,即時擷取的車號資訊,以語音或震動等其他感官方式輸出。實驗中以一般大眾普遍使用鏡頭取得影像資訊,克服以往利用固定鏡頭做處理的方式利用,使用數位相機來模擬可攜式鏡頭,在非固定位置及角度的情況下進行公車區域的分割,利用階段式的處理方法提升系統速度,首先以相鄰相減法,快速擷取前景公車畫面,經過公車幾何分析判定車號所在位置,再利用Sobel測邊定位原理後搭配形態學遮罩,將框取的車號圖片做字元切割及辨識,最後藉由OCR辨識系統搭配MS SAPI 5.1做語音播放系統輸出,在公車停靠前辨識其車號並輸出,實驗畫面為停靠區前約70公尺至公車停靠,實驗中停靠影像時間約為5秒,實驗結果顯示在100張連續測試畫面中約有70張可正確框選出公車區域,其中30張可正確抓取公車車號位置做定位及辨識,且系統每秒可處理31張畫面,可達即時,未來可使用多平台執行,實現方便可攜的輔助性工具來幫助視障者。

    The visually impaired persons may encounter many difficulties when taking a bus. Among them, recognizing the bus number can be the most challenging task for them. Up to now, the ways to solve this problem are to ask for other passengers' help or make use of a self-made board on which shows the bus number to cause the bus driver’s attention. However, both methods are passive and not reliable. This research applies digital image processing technology, through the medium of the camera of up-to-date 3C products such as mobile phone, PDA etc, to help the visually impaired persons to recognize the bus number by senses other than sight. The study aims to delivering in-time bus information with proactive (automatic) identification, fast response without the harm to the accuracy and other sensible outputs such as vibration and sounds. In this experiment, the algorithms solve the problem of fixed lent and are able to segment the bus image with unfixed positions and angles, and speed up the system by a proposed method. First, the system catches the bus image by Frame difference, and identifies the position of bus number through geometry analysis. Then, uses Sobel mask and a location algorithm to segment the bus numbers and recognizes them by using the Optical Character Recognition (OCR). Finally, the system outputs the correct bus number phonetically through Microsoft Speech Application Interface 5.1 (MS SAPI 5.1) before the bus stops. In the experiment, the video was set to film about 70 meters from the bus station. The length of each film was around 5 seconds. Among 100 frames, about 70 ones could segment the bus images correctly, and over 30 bus numbers could be located correctly. The system processing speed is 31 images per second. In the future, this technology can be applied to multiple media and bring the realization of a more convenient and helpful tool for the visually impaired persons.

    目 錄 摘 要 I Abstract II 目 錄 III 圖 目 錄 V 表 目 錄 VIII 第一章  緒論 1 1-1 前言 1 1-2 研究動機 4 1-3 研究背景與目的 6 1-4 系統架構 8 1-5 論文架構 9 第二章  文獻探討與回顧 10 2-1 相關理論 10 2-1-1 色彩空間 10 2-1-2 二值化 12 2-1-3 形態學運算 12 2-2 相關研究 13 2-2-1 無線辨識系統 14 2-2-2 前景擷取 15 2-2-3 車牌辨識 17 2-2-4 光學文字辨識系統 18 2-3 文獻探討 19 2-3-1 無線辨識系統 19 2-3-2 前景擷取 22 2-3-3 定位系統 26 2-3-4 綜合討論 27 第三章  即時辨識公車車號之研究方法 28 3-1 移動物偵測 30 3-1-1 前處理 30 3-1-2 相鄰相減 31 3-1-3 二值化 33 3-1-4 侵蝕 37 3-1-5 膨脹 38 3-1-6 連通物件 41 3-2 辨識區域 43 3-2-1 幾合篩選 43 3-2-2 定位系統 44 3-4 車號辨識 49 第四章  實驗結果與討論 50 4-1 實驗內容 50 4-2 實驗結果分析與比較 66 第五章  結論與未來展望 73 參 考 文 獻 74 圖 目 錄 圖1-1 不同環境下、角度示意圖 7 圖1-2 系統架構流程圖 8 圖2-1 光三原色 10 圖2-2 無線感應發報器 15 圖2-3 前景擷取處理結果 16 圖2-4 RFID系統 19 圖2-5 RFID元件 20 圖2-6 校園公車監控流程圖 20 圖2-7 盲人使用RFID流程圖 21 圖3-1 影像處理流程圖 29 圖3-2 RGB影像 30 圖3-3 相鄰相減法處理結果 32 圖3-4 二值化處理結果 35 圖3-5 侵蝕之示意圖 37 圖3-6 影像侵蝕處理結果 37 圖3-7 膨脹之示意圖 38 圖3-8 膨脹處理結果 38 圖3-9 斷開之示意圖 39 圖3-10 移動區域處理結果 40 圖3-11 八連通遮罩 41 圖3-12 連通標籤之影像 42 圖3-13 連通處理結果 42 圖3-14 前景物範圍比較圖 43 圖3-15 幾何判定處理結果 44 圖3-16 Sobel遮罩 45 圖3-17 Sobel測邊結果 46 圖3-18 定位結果 46 圖3-19 彩色影像處理流程圖 47 圖3-20 RGB成分 48 圖3-21 負片處理 48 圖3-22 分割區域 49 圖3-23 辨識結果 49 圖4-1 實驗環境 51 圖4-2 流程圖 52 圖4-3 移動物偵測結果 53 圖4-4 公車擷取結果 54 圖4-5 定位結果 55 圖4-6 車號資訊 56 圖4-7 車號辨識 56 圖4-8 移動物偵測 57 圖4-9 定位系統 58 圖4-10 彩色資訊 59 圖4-11 車號(不同角度1) 60 圖4-12 車號(不同角度2) 61 圖4-13 移動物偵測 62 圖4-14 車板資訊 63 圖4-15 車號資訊 63 圖4-16 車號(雨天) 64 圖4-17 車號(幹道) 65 圖4-18 模糊不清 70 圖4-19 LED燈損壞 71 圖4-20 車板損壞圖 71 圖4-21 反光 72 表 目 錄 表1-1 影像處理優點 1 表2-1 背景雜訊與前景雜訊特性表 23 表2-2 前景截取比較表 24 表4-1 硬體規格 50 表4-2 公車擷取結果 66 表4-3 號碼辨識結果 67 表4-4 RFID比較結果 68 表4-5 速度比較結果 69

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    其他參考資料
    COULEUR.ORG,http://www.couleur.org/
    台北市政府工務局,http://www.pwb.taipei.gov.tw/
    光學文字辨識系統,http://adc.cm.nsysu.edu.tw/ocr/#2
    JOCR,http://home.megapass.co.kr/~woosjung/Product_JOCR.html

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