以立體視覺實作盲人輔具系統

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姓名

謝易錚(Yi-Zeng Hsieh) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以立體視覺實作盲人輔具系統
(A Stereo-Vision-Based Aid System for the Blind)

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摘要(中)

視障人士位於不熟悉的環境中，想要了解整各環境的概況，必須使用白手杖（White Cane），去觸碰地面或是物體以確認前方是否可以行走以及障礙物的位置，而手杖不能碰觸的地方則無從得知。使用者只有在面臨障礙物時才被輔具所引導閃避，並沒有自主選擇行走路徑的能力，因此希望開發出一套系統，能夠將環境中的資訊，先告知視障人士，並配合白手杖的使用，根據系統的提示，自己選擇需要移動的方向，增加盲人行走的安全與自主性。
本論文中提出以立體視覺的方式，先切割出環境的路面區域，利用影像量化，非路面區域根據量化的影像區塊去做影像比對，利用分群的方法去除影像雜訊，建立出陌生環境資訊，偵測出障礙物體，並指出障礙物離盲人多遠與障礙物方向，當盲人在行走時，可以先預知行走環境的整體資訊，利用此資訊並搭配白手杖的使用，達到盲人行走安全，以達到解決使用者-「障礙物位於哪裡？離障礙物體有多遠？障礙物大小？」的疑惑。藉由此初步的研究，即能令目前導盲的輔具化被動為主動，讓視障人士的行動更為自由。

摘要(英)

White Cane is the most pervasive travel-aid for the blind. We present an idea of using stereo matching to develop a travel aid for the blind. In this approach, first we use a segmentation algorithm to segment the floor region from the image captured by the web camera. Then images are segmented into several non-overlapping homogeneous regions using a color segmentation algorithm. For each homogeneous region, a rectangular window, which is large enough to cover the region, is found. A local match with the found rectangular window size is then executed to find the disparity for the considered region. A clustering algorithm is adopted to cluster the disparities into several major different values. Finally, a piece-wise disparity map is constructed. Based on the disparity map, information about the unfamiliar environments in front of the blind will be output to them. With the information about the environment the blind will have less fear in walking through unfamiliar environments via white canes.

關鍵字(中)

★ 立體視覺
★ 行動輔具

關鍵字(英)

★ stereo matching
★ travel aid

論文目次

摘要 I
ABSTRACT II
第一章緒論 1
1.1 研究動機 1
1.2 研究目標 3
1.3 論文架構 4
第二章導盲輔具介紹與研究 5
2.1 導盲輔具 5
2.2 電子式行進輔具 5
2.3 引導式機器人 6
2.4 穿戴式輔具 8
2.5 導引式手杖 9
2.6 電子晶片或人工視網膜植入 10
2.7 立體視覺輔具 10
2.8 探討導盲輔具 11
第三章研究方法與步驟 14
3.1 以視覺為基礎的導引系統（THE VISION-BASED TRAVEL AID） 14
3.1.1 系統硬體介紹 15
3.1.2 系統軟體演算法 17
3.2 路面影像生長（REGION GROWING） 18
3.2.1 RGB色彩空間 18
3.2.2 HSV色彩空間 19
3.2.3 色彩空間討論 21
3.2.4 切割路面影像 21
3.2.5 影像資訊量化 21
3.2.6 影像資訊減量 22
3.2.7 樣本比對 23
3.2.8 路面區域生長 24
3.3 色彩量化 25
3.3.1 色彩量化演算法分類 26
3.3.2 色彩量化演算法簡介 26
3.3.3 K-means群聚演算法 28
3.3.4 導盲影像色彩量化 31
3.4 影像矯正 34
3.4.1 色彩矯正 34
3.4.2 水平矯正 35
3.5 立體視覺（STEREO VISION） 38
3.5.1 影像深度 38
3.5.2 立體比對 41
3.5.3 選擇立體比對區域大小之探討 43
3.6 不相稱分群（DISPARITY CLUSTERING） 44
3.7 障礙物標示 47
3.8 距離估測 48
3.8.1 指數函數逼近 48
3.8.2 距離估測函數 49
3.9 物體長寬與物體方向判別 52
3.10 系統演算法流程 56
第四章實驗分析與討論 58
4.1 實際環境圖 58
4.2 實際環境障礙物大小距離與方向 60
第五章結論與展望 64
5.1 結論 64
參考文獻 66

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指導教授

蘇木春(Mu-Chun Su)

審核日期

2006-7-24

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