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標題: | 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 |
作者: | Chia-Hsiung Chen 陳嘉雄 |
指導教授: | 陳少傑(Sao-Jie Chen) |
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, edge detection,gaussian smoothing,bayer pattern,color edge,grayscale edge,bayer-pattern edge, |
出版年 : | 2006 |
學位: | 碩士 |
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。 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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34672 |
全文授權: | 有償授權 |
顯示於系所單位: | 電機工程學系 |
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