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研究生:李文加
研究生(外文):Wen-ChiaLee
論文名稱:旋轉不變性樣板比對之研究
論文名稱(外文):A Study on Template Matching with Rotation Invariance
指導教授:陳進興陳進興引用關係
指導教授(外文):Chin-Hsing Chen
學位類別:博士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
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:2013
畢業學年度:101
語文別:英文
論文頁數:91
中文關鍵詞:樣板比對Zernike矩環形投影轉換有界部分相關係數旋轉不變性
外文關鍵詞:Zernike momentscircular projection transformationbounded partial correlationrotation invariance
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  • 下載下載:104
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樣板比對(template matching)是一種應用於信號與影像處理的技術。樣板比對在場景影像中移動樣板,計算它們之間的相似性以尋找所有可能的樣版位置。樣板比對存在的問題之一是在場景影像中存在旋轉的物體。因此,本論文討論旋轉不變性的樣板比對技術並提出兩種快速旋轉不變性樣板比對方法。
在第一種方法,我們提出一個快速次像素(subpixel)精度的樣板比對方法。首先,利用插值法減小場景影像和樣板影像的大小,並使用環形投影轉換 (circular projection transformation)來確定比對的候選點。環形投影轉換的優點是擁有旋轉不變性的特性,並降低了計算複雜度。接著將這些候選點使用Zernike矩執行樣板比對。環形投影轉換比Zernike矩更有效率。因此,在第一階段使用環形投影轉換可以減少計算時間。最後,利用二次多項式匹配法則(second-degree polynomial fitting formula)決定次像素匹配位置。實驗結果證明,提出的次像素樣板比對方法可以達到次像素精度且執行時間顯著減少。這表示此法適合線上的檢測系統。
在第二種方法中,我們結合環形投影轉換和有界部分相關係數 (bounded partial correlation) 方法,提出一個快速樣板比對方法。首先,利用環形投影轉換的核心濾波器求得影像特徵,以減少計算的複雜度。接著利用有界部分相關係數演算法計算相關係數的相似性量測 (normalized correlation similarity measure)以減少執行時間。在一台Pentium IV 3GHz處理器的電腦上,完成單次程序所需要的時間為0.12秒,使用的背景影像大小為 且樣板大小為 。實驗結果證明,提出的樣板比對方法所得的匹配結果與環形投影轉換是一致的,並且大幅降低執行時間。這表示此法適合即時辨識系統。

Template matching is a technique used in signal and image processing. It searches all possible positions of a template in a scene image by moving a template, and computing the similarity measures between them. One of the existing problems in template matching is when rotated objects exist in a scene image. Hence, this thesis discusses many rotation invariance techniques used in template matching and proposes two different approaches for fast template matching with rotation invariance.
In the first approach, a fast template matching method with subpixel accuracy is proposed. In the first stage of the approach, the reduced sizes of the scene image and the template image are used by the interpolation method and the circular projection transformation (CPT) process is used to determine the matching candidates. The advantages of the CPT process are that it owns the property of rotation invariance and reducing the computational complexity. In the second stage of the approach, the Zernike moments were applied to perform template matching only on the matching candidates. CPT is more efficient than the Zernike moments. Hence, using CPT in the first stage can reduce the computation time for selecting the matching candidates. Finally, the subpixel position can be computed by the second-degree polynomial fitting formula. The experimental results show that the proposed subpixel template matching method can reach subpixel accuracy and the execution time is significantly reduced. This indicates that our approach is suitable for online template matching.
In the second approach, a fast template matching method by combining CPT process and the bounded partial correlation (BPC) algorithm was proposed. In the first stage of the approach, the CPT kernel filters are used to reduce the time complexity of computing CPT values. In the second stage of the approach, the BPC algorithm was used to reduce the execution time of the normalized correlation similarity measure. The experimental results show that the matching result from our method was consistent with that of the CPT, and the execution time was considerably reduced. The computational time of the proposed method takes approximate 0.12 seconds to complete entire operation with a 3 GHz Pentium IV PC using the program Visual C++ for a scene image and a template. This indicates that our approach is suitable for online template matching.

摘 要 i
Abstract iii
誌 謝 v
Contents vi
Table Captions ix
Figure Captions xi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Work 3
1.3 An Overview of the Thesis 9
1.4 Organization of the Thesis 10
Chapter 2 Techniques of Template Matching 12
2.1 Introduction 12
2.2 Similarity Measures 14
2.3 Projection Transformation 15
2.3.1 Circular Projection Transformation 16
2.3.2 Radial Projection Transformation 21
2.3.3 Summary 26
2.4 Image Moments 29
2.4.1 Hu Moments 30
2.4.2 Zernike Moments 35
2.4.3 Orthogonal Fourier-Mellin Moments 40
2.5 Elimination Algorithms 45
2.5.1 Successive Elimination Algorithm 46
2.5.2 Bounded Partial Correlation 47
Chapter 3 A Template Matching Algorithm Using CPT and Zernike Moments 49
3.1 Introduction 49
3.2 Procedure of Template Matching of the Proposed Method 50
3.2.1 Acquiring Images 52
3.2.2 Teaching 53
3.2.3 Coarse Search 53
3.2.4 Fine Search 58
3.2.5 Registering in Subpixel Accuracy 60
3.3 Experimental Results and Discussion 61
3.4 Summary 65
Chapter 4 A Template Matching Algorithm by Combining CPT and BPC 67
4.1 Introduction 67
4.2 The Proposed Algorithm of Template Matching by the Combination of CPT and BPC 68
4.2.1 Acquiring Images 69
4.2.2 Teaching 69
4.2.3 Circular Projection Transformation Using Kernel Filters 69
4.2.4 Bounded Partial Correlation Using CPT 70
4.3 Procedure of Template Matching of the Proposed Method 71
4.4 Experimental Results and Discussion 73
4.5 Summary 79
Chapter 5 Conclusions and Future Work 81
References 83

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