NDLI: An improved edge detection algorithm based on fuzzy theory

Content Provider	IEEE Xplore Digital Library
Author	Longtao Zhang Yuqiu Sun Fushan Chen
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. & Math., Yangtze Univ., Jingzhou, China (Longtao Zhang; Yuqiu Sun; Fushan Chen)
Abstract	There exists serious distortion in Pal-King edge detection algorithms. In order to solve the problem, an improved method based on fuzzy theory and the maximum between cluster variance is presented by studying Pal.King algorithm synthetically. Firstly, the histogram of an image is calculated and researched. If there is one peak in the image histogram, the Pal-King operator can be used to process the image directly. Otherwise, the fuzzy threshold is set to enhance the image. Secondly, the image is filtered by the Gaussian filter. Then the non-maximum suppression method is applied to locate the edge and process the gradient magnitude. Thirdly, a two-threshold method is used to detect and connect the image edge. At last, a series of experiments have been done to compare this new algorithm with Sobel operators, Pal-King operators and Canny operators. It is shown from the experimental results that the method proposed in this paper is efficient.
Starting Page	380
Ending Page	384
File Size	1177822
Page Count	5
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7381972
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	fuzzy enhancement Histograms Canny operators Image edge detection Clustering algorithms Distortion Iron Entropy edge detection Pal-King operator maximum between-cluster variance Sun
Content Type	Text
Resource Type	Article

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