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Proceedings Paper

New method for edge detection and localization
Author(s): Jeng-Feng Lee; Yuan-Fang Wang; Ping Liang
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Paper Abstract

In this paper, we develop a technique which applies the Green’s theorem to locate edges in images. Our method implements the traditional Laplacian of Gaussian operator over different resolution scales for edge detection. However, only the first derivatives of an image function—not the second-derivative Laplacian operators—are computed in our method. Gaussian kernels of different sizes are convolved with a raw image to generate smoothed images at different resolution scales. The first derivatives are calculated from the smoothed images. Equi-first- derivative pixel pairs in both the x and y directions are located. They are then grouped into closed contours in the derivative maps. The Green’s theorem states that if the Laplacian operator produces a smooth, continuous function, zero-crossings (edge points) will be enclosed in these equi-first-derivative contours. Implementation results show that our technique is capable of locating edges at different scales.

Paper Details

Date Published: 1 January 1990
PDF: 12 pages
Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21101
Show Author Affiliations
Jeng-Feng Lee, Univ. of California/Santa Barbara (United States)
Yuan-Fang Wang, Univ. of California/Santa Barbara (United States)
Ping Liang, Technical Univ. of Nova Scotia (Canada)


Published in SPIE Proceedings Vol. 1293:
Applications of Artificial Intelligence VIII
Mohan M. Trivedi, Editor(s)

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