Paper Info
Title
Competitive fuzzy edge detection
Abstract
Our fuzzy classifier detects classes of image pixels corresponding to gray level variation in the various directions. It uses an extended Epanechnikov function as a fuzzy set membership function (FSMF) for each class where the class assigned to each pixel is the one with the greatest fuzzy truth of membership. This classification is done first, after which a competition is run as a second step to thin the edges. Like the Canny edge detector, the edge sensitivity of our competitive fuzzy edge detector (CFED) can be set from low to high by the user. The performance of our algorithm is somewhat similar to that of the Canny algorithm but ours is significantly faster. For both, the proper level of sensitivity must be chosen by the user for the best results because the tradeoff is more edges with more noise versus fewer edges and less noise. However, the settings are less sensitive and more intuitive for our algorithm. We make comparisons on good and degraded images.
Year
DOI
Venue
2003
10.1016/S1568-4946(03)00008-5
Applied Soft Computing
Keywords
Field
DocType
Fuzzy classifier,Edge detection,Competitive edge selection
Canny edge detector,Pattern recognition,Defuzzification,Edge detection,Fuzzy logic,Fuzzy set,Pixel,Artificial intelligence,Fuzzy number,Membership function,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
3
2
1568-4946
Citations 
PageRank 
References 
53
3.04
13
Authors
2
Name
Order
Citations
PageRank
Lily Rui Liang1543.40
Carl G. Looney219821.58