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170306s2017 gw | s |||| 0|eng d |
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|a 9783319539942
|9 978-3-319-53994-2
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|a 10.1007/978-3-319-53994-2
|2 doi
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|a 006.3
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|a Gonzalez, Claudia I.
|e author.
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|a Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic
|h [electronic resource] /
|c by Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Oscar Castillo.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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|a X, 89 p. 34 illus., 21 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a SpringerBriefs in Applied Sciences and Technology,
|x 2191-530X
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|a Introduction -- Generalized Type-2 Fuzzy Logic -- Edge Detection Methods and Filters Used on Digital Image Processing -- Metrics for Edge Detection Methods -- Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic Systems -- Generalized Type-2 Fuzzy Edge Detection Applied on a Face Recognition System -- Experimentation and Results Discussion -- Conclusions.
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|a In this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format. 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.
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|a Engineering.
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|a Artificial intelligence.
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|a Pattern recognition.
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|a Computational intelligence.
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|a Engineering.
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|a Computational Intelligence.
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|a Artificial Intelligence (incl. Robotics).
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|a Pattern Recognition.
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|a Melin, Patricia.
|e author.
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|a Castro, Juan R.
|e author.
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|a Castillo, Oscar.
|e author.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783319539935
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|a SpringerBriefs in Applied Sciences and Technology,
|x 2191-530X
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|u http://dx.doi.org/10.1007/978-3-319-53994-2
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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|a Engineering (Springer-11647)
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