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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃乾綱(Chien-Kang Huang) | |
dc.contributor.author | Chia-Jung Chen | en |
dc.contributor.author | 陳加容 | zh_TW |
dc.date.accessioned | 2021-06-15T11:18:04Z | - |
dc.date.available | 2017-08-31 | |
dc.date.copyright | 2016-08-31 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-19 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49168 | - |
dc.description.abstract | 近年來隨著科技的進步、無線感知設備與行動裝置普及,陸續發展眾多空間定位的應用服務。談到定位與導航的應用,人類現今在室外最常使用即是全球定位系統 (Global Positioning System, GPS)。人類在生活中,有很大一大部分的時間都從事室內活動,但由於室內環境的限制,使得 GPS 無法在室內空間中做精確定位服務,進而產生出室內定位服務的需求。
本論文利用人類在面對陌生公共環境,且 GPS 無法提供有效服務時,亦採取尋找方向告示牌、警示牌、路標或室內地圖…等文字說明訊息的行為,提出一演算法以模擬人類在陌生公共室內環境時,會採取的視覺影像資訊檢索暨定位策略做為導航之用。 本論文以行動設備的相機模擬人類視覺的感知設備,在室內空間中拍攝具有文字資訊的方向指示牌,接著透過影像處理最大穩定極值區域 (MSER) 的方法,偵測影像中具有資訊的區域,經過演算法的計算,擷取出影像中的文字資訊區域,再利用尺度不變特徵轉換 (SIFT) 匹配預先建立的室內文字影像地圖,以達到室內空間定位的目的。 本論文之演算法在未經過數據訓練的情況下,文字影像偵測正確率達到 74.84%,且影像是由行動設備在人潮眾多的台北車站捷運站內以及其它大眾運輸室內環境所拍攝。而在文字影像空間定位實驗中,室內定位的正確率達 79.89%。證實本論文之演算法在無建置室內空間模型、訓練數據與控制環境條件下,亦可以達到室內空間定位的目的。 | zh_TW |
dc.description.abstract | Outdoor GPS is the backbone of positioning and navigation applications, today. However, people engage in indoor activities much more than outdoor activities in an urban environment. Unfortunately, GPS can not provide precise positioning service in an indoor setting. Therefore, the need for indoor positioning services emerges.
The approach proposed in this study mimics human’s natural behavior to find the directions from traffic signs, warning signs, indoor maps in an unfamiliar public environment without precise GPS service. In other words, this study takes the strategy of retrieving image and positioning information for the purpose of navigation in an unfamiliar public indoor environment. This study takes mobile device's camera as human visual input and takes pictures of the direction signs with text information in indoor space. Then, a MSER-based feature detector is adopted to detect image regions with information as candidates. The algorithm extracts the images of the text information area with SIFT feature detection to match pre-established text image maps to serve the purpose of spatial information retrieval. The algorithm proposed in this study does not require any training works before installation, therefore it saves training time and avoids the overfitting problem. The images were taken with a mobile device in Taipei MRT Station and other public transport indoor environment. The text detection experiment has 74.84% precision. Basing on the experiment, a further text-image spatial positioning experiment is conducted and reached 79.89% precision. According to the results, the proposed algorithm without building a spatial model and training data in advance is robust and applicable for uncontrolled environmental image inputs. In other words, it can be the core part of a service for an indoor positioning system in the future. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T11:18:04Z (GMT). No. of bitstreams: 1 ntu-105-R03525060-1.pdf: 6612689 bytes, checksum: b4b2cf138673b3ef9d92fc907cd045fc (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 中文摘要 iii Abstract iv 目錄 vi 圖目錄 viii 表目錄 x 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究貢獻 2 1.4 論文架構 3 第二章 文獻探討 4 2.1 文字影像擷取方法之探討 4 2.1.1 基於影像紋理法 5 2.1.2 基於影像區域法 6 2.2 MSER 演算法之基礎理論 9 2.2.1 最大穩定極值區域 (Maximally Stable Extremal Regions, MSER) 9 2.2.2 Linear Time Maximally Stable Extremal Regions 11 2.3 特徵偵測之探討 12 2.3.1 Features from Accelerated Segment Test (FAST) 12 2.3.2 Scale-Invariant Feature Transform (SIFT) 13 2.4 基礎影像處理 16 2.4.1 邊緣偵測法 (Edge Detection) 16 2.4.2 雙邊濾波法 (Bilateral Filter, BF) 17 2.4.3 二值化 (Threshold) 18 第三章 研究方法 21 3.1 問題定義及研究架構 21 3.1.1 問題定義 21 3.1.2 研究架構 22 3.2 偵測空間中的資訊位置 23 3.3 計算幾何 24 3.3.1 刪除非文字的候選區域 24 3.3.2 合併候選區域 25 3.3.3 擷取候選區域之外輪廓 26 3.3.4 計算候選區域亮度分佈差異 29 3.3.5 組合候選區域 30 3.4 文字影像特徵偵測 32 3.4.1 偵測文字角點特徵 32 3.4.2 發光文字光暈處理 34 3.4.3 文字邊緣強度投影 35 3.5 文字影像空間定位 37 第四章 實驗結果與討論 39 4.1 實驗資料收集與建置 39 4.2 實驗評估方法 40 4.3 文字影像區域偵測結果 41 4.4 文字影像空間定位結果 45 第五章 結論與未來展望 48 5.1 結論 48 5.2 未來展望 49 參考文獻 50 附錄一 文字影像特徵偵測 54 附錄二 台北車站捷運站的告示牌基準位置 59 | |
dc.language.iso | zh-TW | |
dc.title | 基於手持移動裝置之室內空間文字影像擷取 | zh_TW |
dc.title | Text Images Based Spatial Information Retrieval with Handheld Mobile Devices | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張恆華(Herng-Hua Chang),傅楸善(Chiou-Shann Fuh) | |
dc.subject.keyword | 室內定位,文字影像偵測,最大穩定極值區域,尺度不變特徵轉換, | zh_TW |
dc.subject.keyword | Indoor Localization,Text Detection,MSER,SIFT, | en |
dc.relation.page | 60 | |
dc.identifier.doi | 10.6342/NTU201602757 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-20 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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