臺灣博碩士論文加值系統

本文提出一個新的邊緣偵測演算法。啟發自CFA 插補(interpolation)法，本文提出可以在拜耳圖樣(Bayer pattern)上執行類高斯平滑(Gaussian smoothing)及類拉氏(Laplacian)邊緣偵測之演算法。經由適當延伸，本演算法亦可偵測彩色及灰階影像之邊緣。使用本演算法之優點包括插補運算及色彩轉換運算之節省，並能節省記憶體使用量。在邊緣偵測演算法部分，相較於5×5 高斯-拉氏遮罩(Laplace of Gaussian mask)，本文提出之方法在彩色邊緣偵測上可節省約5/6 之運算量，相較於零點偵測法(zero-crossing detection) 可節省2/3 之運算量，於灰階邊緣偵測亦可節省約1/3 之高斯-拉氏遮罩運算。實驗顯示本文提出之演算法有不錯的偵測結果，並可藉由調整標準差及門檻值兩參數亦可增加此演算法之適應性。

A new edge detection algorithm is proposed in this Thesis. Inspired by the Color Filter Array (CFA) interpolation kernels, we design two other kernels for the algorithm to perform Gaussian-like smoothing and Laplacian-like edge detection directly on a Bayer-patterned image. Also, the proposed algorithm can be easily extended to existing color and grayscale images. That is, it is capable of detecting edges in a Bayer-patterned, a color, or a grayscale image.
Benefits of performing edge detection on a Bayer-patterned image include the computation saving of the interpolation and/or color space transform to a full color or grayscale image, and lower memory usage. With the proposed 5×5 kernels, the extension to color edge detection theoretically presents approximately 5/6 of computation saving from the existing color Laplace of Gaussian (LOG) operations, and 2/3 saving from the three-channel zero-crossing detection, while for grayscale edge detection presents approximately 1/3 of computation saving from the existing grayscale LOG operation. Experimental results show that the proposed algorithm has great localization and flexibility by tuning its standard deviation σ and threshold parameter th.

ABSTRACT i
LIST OF FIGURES v
CHAPTER 1 INTRODUCTION 1
1.1 Edge Detection and its Applications 1
1.2 Image Processing Pipeline in Digital Still Cameras 3
1.3 Prior Arts 5
1.3.1 Grayscale Edge Detection Algorithms 5
1.3.2 Color Edge Detection Algorithms 6
1.3.3 Bayer Pattern Related Image Processing Algorithms 6
1.4 Problem Formulation and Contributions 7
1.5 Thesis Organization 9
CHAPTER 2 BACKGROUND INFORMATION 11
2.1 Spatial Domain Image Processing 11
2.2 Smoothing in Spatial Domain 14
2.3 Grayscale Edge Detection Fundamentals 17
2.4 Color Edge Detection Fundamentals 20
2.5 Bayer Pattern Interpolation Fundamentals 21
CHAPTER 3 CFA INTERPOLATION INSPIRED EDGE DETECTION 27
3.1 Algorithm Overview 27
3.2 On Gaussian Smoothing 28
3.3 On Edge Detection 34
3.3.1 EDP1: Laplacian Part 35
3.3.2 EDP2: Zero Crossing Part 38
3.3.3 EDP3: Output and Thresholding Part 39
3.3.4 The Complete Edge Detection Flow 40
3.4 Extension to Color and Grayscale Images 42
CHAPTER 4 EXPERIMENTAL RESULTS AND DISCUSSION 45
4.1 Smoothing Filter Performance Evaluation 48
4.2 Edge Detector Performance Evaluation 52
4.3 Theoretical Computation Savings 59
CHAPTER 5 CONCLUSION 61
REFERENCE 63

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