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研究生:吳瑞珍
研究生(外文):Jui-Chen Wu
論文名稱:植基於顯著特徵抽取技術之視訊內容分析系統
論文名稱(外文):Salient Features Extraction for Video Content Analysis
指導教授:陳永盛陳永盛引用關係
學位類別:博士
校院名稱:元智大學
系所名稱:電機工程學系
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:2009
畢業學年度:97
語文別:英文
論文頁數:81
中文關鍵詞:型態學基礎文字偵測與辨識特徵值顏色基礎車子角度的偵測車輛的檢索
外文關鍵詞:morphology-basedtext detection and recognitioneigen color extractionvehicle orientation analysisvehicle retrieval
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科技的進步帶動多媒體廣泛的使用,為了有效地傳輸、儲存與檢索視訊資料,發展一套理想的物件偵測與分析顯得非常重要。為達此目的,在過去十年中,已經針對這方面提出許多相關問題。因此,若能詳盡分析平面與視訊影像中的物件,能將提供使用者極為豐富的資訊。
在本論文中,我們主要提出兩個有效顯著特徵抽取的方法,針對視訊內容中的平面中文字進行偵測與辨識,以及在交通安全系統中的車輛方向定位與資料庫中的車輛檢索。第一,我們利用“型態學基礎”的方法,此方法是利用影像中前景與背景有顯著明亮度的對比的特色,將抽取出前景的區域,對於文字偵測與辨識之中,我們將其特徵抽取之後,利用其本身的幾何特性進行文字列的偵測,接而進行影像中的車牌辨識。第二,為了將在交通安全系統中,快速偵側車輛的定位與資料庫中車輛的檢索,我們提出了一個 “特徵值顏色基礎”的方法,此方法針對某特定物體進行統計上的分析所推導得到的結果,在這個新的特徵色彩空間上,前景物像素點可以容易地與背景物的像素點作區分,即使是在一些具有光線變化的場景。抽取出候選區之後,我們將一些描述例子分別進行車子角度的偵測與車輛的檢索。實驗結果證明我們所提出的方法有不錯的效果。
Multimedia can be widely used due to the progress of science and technology. An automatic object detection and analysis system is necessary that we can ability transmits, store and retrieve for video data. To achieve these goals, there have been many approaches proposed for detecting object for a plan or visual median. Hence, having a detailed description of video content analysis can provide rich information for user.
In this thesis, we will propose two novel salient features extraction schemes for text detection and recognition in images or video sequences, vehicle orientation analysis and vehicle retrieval from image databases. First, the morphology-based scheme can be used to find out high contrast region with their background. The method is invariant under different lighting, scaling, and viewing conditions. As a result of text often having high contrast with their background, all possible candidate regions will be extracted. Finally, the geometric properties can be used to detect text line from images. After extracting, we will recognize license plate from video sequences. Moreover, to fast and effectively analyze vehicles from image databases, we proposed the “eigen” color extraction scheme to detect possible vehicle regions from cluttered images. The model can efficiently separate foreground pixels from the cluttered images even under different lighting conditions. After extracting candidates regions, we will define some descriptors to achieve vehicle orientation analysis and vehicle retrieval system. Experimental results reveal the superior performances in text extraction, license plate recognition, vehicle orientation analysis, and vehicle retrieval.
Content
摘 要 i
Abstract ii
誌 謝 iv
List of Figure vii
List of Table x
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Review of Related Works 4
1.2.1 Previous Methods for Text Line Extraction 4
1.2.2 Previous Methods for License Plate System 5
1.2.3 Previous Methods for Vehicle Orientation Analysis 6
1.2.4 Previous Methods for Vehicle Retrieval 7
1.3 Overview of Approach 10
1.3.1 Text Line Extraction System 10
1.3.2 License Plate Recognition System 11
1.3.3 Vehicle Orientation Analysis System 11
1.3.4 Vehicle Retrieval System 12
1.4 Organization of the Dissertation 13
Chapter 2 Morphology-based Scheme 14
2.1 Text Line Detection 15
2.1.1 Feature Extraction 15
2.1.2 Text Line Selection 17
2.1.3 Text Line Verification 21
2.1.4 Text Line Extraction Performance 25
2.1.5 Summary 33
2.2 License Plate Recognition 34
2.2.1 License Plate Detection 34
2.2.2 License Plate Recognition 37
2.2.3 License Plate Recognition Performance 40
2.2.4 Summary 42
Chapter 3 Eigen Color Extraction Method 44
3.1 Vehicle Orientation Analysis 46
3.1.1 Vehicle Representation 46
3.1.2 Spectral Clustering 52
3.1.3 Vehicle Orientation Analysis Performance 55
3.1.4 Summary 63
3.2 Vehicle Retrieval 64
3.2.1 Feature Selection 64
3.2.2 Multiple-Instance Learning 65
3.2.3 Vehicle Retrieval Performance 67
3.2.4 Summary 70
Chapter 4 Discussion 71
Chapter 5 Conclusions and Future Work 73
5.1 Conclusions 73
5.2 Future Work 74
References 75
Publication List 81
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