Edge Detection Algorithm and its Gaussian Smoothing Filter Design

• Main Authors: Chia-Hsiung Chen, 陳嘉雄
• Other Authors: Sao-Jie Chen
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/65312551668687735767

碩士 === 國立臺灣大學 === 電機工程學研究所 === 94 === 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.