邊緣偵測演算法及其高斯平滑濾波器設計

Edge Detection Algorithm and its Gaussian Smoothing Filter Design

陳嘉雄(Chia-Hsiung Chen)

指導教授 ： 陳少傑

國立臺灣大學/電機資訊學院/電機工程學研究所/碩士(2006年)

https://doi.org/10.6342/NTU.2006.01299

摘要

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。

關鍵字

邊緣偵測 ； 高斯平滑 ； 拜耳圖樣 ； 彩色邊緣 ； 灰階邊緣 ； 拜耳圖樣邊緣

並列摘要

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.

並列關鍵字

edge detection ； gaussian smoothing ； bayer pattern ； color edge ； grayscale edge ； bayer-pattern edge

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