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研究生:鄭超鴻
研究生(外文):Chau-Hung Cheng
論文名稱:使用模糊理論基於格式塔心理學原理對立體視覺匹配之研究
論文名稱(外文):A Study on Stereo Matching Using Fuzzy Theory Based on Gestalt Principles
指導教授:駱榮欽駱榮欽引用關係
口試委員:林啟芳王振興
口試日期:2012-07-27
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
學門:工程學門
學類:機械工程學類
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:42
中文關鍵詞:立體立體場景分析視差影像格式塔心理學模糊理論
外文關鍵詞:StereoStereo scene analysisDisparity mapGestalt psychologyFuzzy theory
相關次數:
  • 被引用被引用:1
  • 點閱點閱:435
  • 評分評分:
  • 下載下載:24
  • 收藏至我的研究室書目清單書目收藏:0
在立體視覺中,如何產生正確且清晰的視差影像是現今熱烈被探討的議題。在傳統局部區域匹配演算法上,如何依據不同的影像特性選擇適合的搜尋視窗大小是一個讓人難以取捨的問題。本論文導入格式塔心理學來增加基於局部區域匹配演算法所完成的視差圖之精確性,利用模糊理論在解決對於人類語言特有的模糊性現象有頗佳的效果,將其用以更精確的描述完形心理學上對於相似度及相近度之探討。實驗結果顯示,我們所提出的演算法成功的結合了模糊理論及格式塔心理學原理,並且在重覆紋理的地區所產生之錯誤率較一般自適應性搜尋視窗演算法來得低。

One of most popular issues in stereo vision is how to increase the accuracy of the disparity map. On the traditional area-based local methods, selection on the appropriate search window size based on different image features still debate in recently. This study aims to adopt the Gestalt psychology to increase the accuracy of the disparity map by using fuzzy theory. Fuzzy theory has great effects in solving the ambiguity of linguistic. Therefore, we apply Fuzzy theory to explain the Gestalt psychology on the character of the similarity and the proximity. The result indicated that our method successfully combines the fuzzy theory and Gestalt psychology. The proposed method is better than other window-based methods in the repetitive pattern region.

摘 要 i
ABSTRACT ii
誌 謝 iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
Chapter 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Related research 1
1.3 Proposed approach 2
1.4 Organization of Thesis 2
Chapter 2 STEREO VISION 4
2.1 Camera Model and Epipolar Geometry 4
2.2 Image Rectification 8
2.3 Flow Chart of Stereo Correspondence 9
2.4 Common Assumptions of Stereo Matching 10
Chapter 3 LOCAL METHOD OF STEREO MATCHING 11
3.1 Traditional Local Method 11
3.2 The Problem of Traditional Local Method 13
3.2.1 The Problem of Low textured Regions 13
3.2.2 Aperture Problem 13
3.2.3 The Problem of Repetitive Patterns 14
3.2.4 The Problem of Foreground Fattening 14
3.3 Adaptive Window Method 15
Chapter 4 FUZZY THEORY 17
4.1 Fuzzifier 17
4.2 Fuzzy Inference System 18
4.3 Defuzzifiers 19
4.3.1 Weighted average 19
4.3.2 Center of Gravity 19
4.4 Sugeno Fuzzy Model 20
Chapter 5 ADAPTIVE SUPPORT-WEIGHT COMPUTATION BASED ON FUZZY THEORY 21
5.1 Group Matching Unit by Human Visual System 21
5.1.1 Similarity 22
5.1.2 Proximity 22
5.2 Fuzzification of the Similarity and the Proximity 23
5.3 Apply Sugeno FIS to Obtain the Weight of Each Pixel in the Search Window 24
5.4 Dissimilarity Computation Based on the Support Weight 29
Chapter 6 EXPERIMENT RESULTS AND DISCUSSION 30
6.1 Experiment Result 30
6.2 Compare with Other Method 33
Chapter 7 CONCLUSIONS AND FURTHER RESEARCH 37
7.1 Conclusions 37
7.2 Further research 37
REFERNCES 38
APPENDIX 40
A.1 Essential Matrix Math 40
A.2 Fundamental Matrix Math 42


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