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Content Provider | IEEE Xplore Digital Library |
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Author | Setayesh, M. Mengjie Zhang Johnston, M. |
Copyright Year | 2010 |
Description | Author affiliation: School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, 6140, New Zealand (Setayesh, M.; Mengjie Zhang) || School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, 6140, New Zealand (Johnston, M.) |
Abstract | Traditional edge detection approaches often result in broken edges especially in noisy images. This study presents a particle swarm optimisation based algorithm to detect edges continuously in such images. In this paper, a new objective function and a new encoding scheme are proposed to address noise and reduce broken edges. The newly developed algorithm is compared to a modified version of the Canny algorithm as a Gaussian-based edge detector based on Pratt's figure of merit measure. Experimental results indicate that the newly developed algorithm can perform better than the Canny and Sobel algorithms in the images. |
Starting Page | 1 |
Ending Page | 8 |
File Size | 3240863 |
Page Count | 8 |
File Format | |
ISBN | 9781424496297 |
ISSN | 21512205 |
e-ISBN | 9781424496310 |
DOI | 10.1109/IVCNZ.2010.6148810 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-11-08 |
Publisher Place | New Zealand |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Shape Image edge detection particle swarm optimisation Manuals Educational institutions Canny edge detection Equations Optimization AI approaches to computer vision Detectors noise edge detection edge linking techniques |
Content Type | Text |
Resource Type | Article |
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