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Proceedings Paper

An edge detection algorithm based on rectangular Gaussian kernels for machine vision applications
Author(s): Fuqin Deng; Kenneth S. M. Fung; Jiangwen Deng; Edmund Y. Lam
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Paper Abstract

In this paper, we develop rectangular Gaussian kernels, i.e. all the rotated versions of the first order partial derivatives of the 2D nonsymmetrical Gaussian functions, which are used to convolve with the test images for edge extraction. By using rectangular kernels, one can have greater flexibility to smooth high frequency noise while keeping the high frequency edge details. When using larger kernels for edge detection, one can smooth more high frequency noise at the expense of edge details. Rectangular kernels allow us to smooth more noise along one direction and detect better edge details along the other direction, which improve the overall edge detection results especially when detecting line pattern edges. Here we propose two new approaches in using rectangular Gaussian kernels, namely the pattern-matching method and the quadratic method. The magnitude and directional edge from these two methods are computed based on the convolution results of the small neighborhood of the edge point with the rectangular Gaussian kernels along different directions.

Paper Details

Date Published: 3 February 2009
PDF: 8 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510N (3 February 2009); doi: 10.1117/12.805241
Show Author Affiliations
Fuqin Deng, The Univ. of Hong Kong (Hong Kong, China)
Kenneth S. M. Fung, ASM Assembly Automation Ltd. (Hong Kong, China)
Jiangwen Deng, ASM Assembly Automation Ltd. (Hong Kong, China)
Edmund Y. Lam, The Univ. of Hong Kong (Hong Kong, China)


Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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