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研究生:連昇鴻
研究生(外文):Sheng-Hung Lien
論文名稱:應用粒子群演算法於區域導向影像定位效能評估
論文名稱(外文):Performance Evaluation on Area-Based Image Registration Using Particle Swarm Optimization
指導教授:范書楷
指導教授(外文):Shu-Kai Fan
口試委員:邱垂昱田方治蔡篤銘
口試委員(外文):Chui-Yu ChiuFang-Chih TienDu-Ming Tsai
口試日期:2012-06-12
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理系碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:113
中文關鍵詞:影像定位粒子群演算法交叉相關性共同資訊量實驗設計
外文關鍵詞:Image RegistrationParticle Swarm OptimizationCross-correlationMutual InformationNormalize Mutual InformationDesign of Experiments
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影像定位主要是透過兩張或多張影像間共同的資訊將影像結合,這些影像可以由不同的觀測點、不同的時間或是不同的感應器所取得。影像定位主要可以分為四個步驟:影像特徵偵測、特徵配對、轉換方程式估計轉換量以及影像修補。而影像定位之方法主要可分為特徵導向(feature based)和區域導向(area based)。本論文所討論的影像定位方式是區域導向,透過交叉相關性(Cross-correlation)、共同資訊量(Mutual Information)找到影像間相似性最高的區域。本研究中將利用粒子群演算法對轉換方程式估計轉換量進行最佳化,把粒子群演算法之參數進行參數實驗設計,進而提供參數給粒子群演算法應用於影像定位,以提高影像定位之準確率,在此將測試五種類別影像分別為:醫療影像、自然影像、空照圖、大位移影像、高對比影像;尋找一組參數組合均適用於交叉相關性(Cross-correlation)、共同資訊量(Mutual Information)、標準化共同資訊量(Normalize Mutual Information) 三種目標函式方法,最後再與先前應用粒子群演算法於影像定位的文獻比較成功率以及運算效率。

Image registration is an important image processing step of aligning two or more images by means of their common information. These images should be taken from different viewpoints, times, or sensors. The procedure of image registration consists of four basic steps: feature extraction, feature matching, transformation model estimation, and image resampling. Image registration methods are classified as feature based and area based methods. The area based methods will be the focus in this thesis. Most cross-correlation and mutual information methods are developed based on image intensities for the direct matching purpose. In this thesis, particle swarm optimization will be utilized to solve the transformation model, and the design of experiments will be conducted for particle swarm optimization, providing a parameter set to improve the registration rate. All the methods considered will be tested for five categories of image sets: medical image, nature image, remote sensing, large translation image and high contrast image. The parameter setting well adapted to cross-correlation, mutual information, and normalize mutual information objective function methods is investigated. At last, the comparison results among the studied methods are reported in terms of the success rate and the number of function evaluations.

Table of Content
摘要 i
ABSTRACT ii
Table of Content iv
List of Figures vi
List of Tables x
Chapter 1 Introduction 1
1.1 Introduction and Backgrounds 1
1.2 Motivation and Description of the Research Method 3
1.3 Organization of the Thesis 5
Chapter 2 Literature Review 6
2.1 Feature Detection 6
2.1.1 Area-based Method 7
2.1.2 Feature-based Method 7
2.2 Feature Matching 9
2.2.1 Area-based Method 9
2.2.2 Feature-based Method 13
2.3 Transformation Model Estimation 14
2.3.1 Similarity Transformation 15
2.3.2 Affine Transformation 16
2.3.3 Projective Transformation 17
2.4 Image Resampling 17
2.5 Optimization of Similarity Metrics 19
2.5.1 Particle Swarm Optimization 20
Chapter 3 Use of Particle Swarm Optimization in Image Registration 23
3.1 Use of Particle Swarm Optimization in Image Registration 25
3.1.1 Cross-correlation 25
3.1.2 Mutual Information 26
3.1.3 Normalized mutual information 27
3.2 Transformation Model 27
3.2.1 Similarity Transformation 28
3.3 Optimization Method 28
3.3.1 Particle Swarm Optimization Method 29
3.3.2 Parameter Setting 30
Chapter 4 Experimental Results 33
4.1 Design and Analysis of Experiment 33
4.1.1 Experimental Result 36
4.1.2 Summary 73
4.2 Performance Comparison 74
Chapter 5 Conclusions and Future Research 109
References 111

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