Overview
- Includes a comparative study of type-1, interval type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphological gradient and the Sobel operator
- Shows the advantage of using a generalized type-2 fuzzy edge detector in pattern recognition applications
- Recent research on Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents(8 chapters)
About this book
Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preprocessing phase of a face rec-ognition system; where the recognition system is based on a monolithic neural network. The aim of this part of the book is to show the advantage of using a generalized type-2 fuzzy edge detector in pattern recognition applications.
The main goal of using generalized type-2 fuzzy logic in edge detec-tion applications is to provide them with the ability to handle uncertainty in processing real world images; otherwise, to demonstrate that a GT2 FS has a better performance than the edge detection methods based on type-1 and type-2 fuzzy logic systems.
Authors and Affiliations
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School of Engineering, University of Baja California, Tijuana, Mexico
Claudia I. Gonzalez, Juan R. Castro
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Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico
Patricia Melin, Oscar Castillo
Bibliographic Information
Book Title: Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic
Authors: Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-319-53994-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2017
Softcover ISBN: 978-3-319-53993-5Published: 14 March 2017
eBook ISBN: 978-3-319-53994-2Published: 05 March 2017
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: X, 89
Number of Illustrations: 13 b/w illustrations, 21 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Pattern Recognition