Edge Detection Algorithm and its Gaussian Smoothing Filter Design
Date Issued
2006
Date
2006
Author(s)
Chen, Chia-Hsiung
DOI
en-US
Abstract
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.
Subjects
邊緣偵測
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
Type
thesis
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