Fuzzy Logic for Image Processing A Gentle Introduction Using Java

141 Pages·2016·5.379 MB·English

by  Caponetti, Laura, Castellano, Giovanna

SPRINGER BRIEFS IN
ELECTRICAL AND COMPUTER ENGINEERING
Laura Caponetti
Giovanna Castellano
Fuzzy Logic for
Image Processing
A Gentle
Introduction Using
Java
123
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SpringerBriefs in Electrical and Computer
Engineering
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Laura Caponetti Giovanna Castellano
(cid:129)
Fuzzy Logic for Image
Processing
A Gentle Introduction Using Java
123
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Laura Caponetti Giovanna Castellano
Dipartimento di Informatica Dipartimento di Informatica
Universitàdegli Studi diBari AldoMoro Universitàdegli Studi diBari AldoMoro
Bari Bari
Italy Italy
ISSN 2191-8112 ISSN 2191-8120 (electronic)
SpringerBriefs inElectrical andComputer Engineering
ISBN978-3-319-44128-3 ISBN978-3-319-44130-6 (eBook)
DOI 10.1007/978-3-319-44130-6
LibraryofCongressControlNumber:2016947389
©TheAuthor(s)2017
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of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this
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To Alfred, Philip and Vito and all my
friends
Laura Caponetti
To David and Serena, improved images
of me
Giovanna Castellano
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Preface
Thesecrettogettingaheadisgettingstarted.
MarkTwain
Thisbookistheresultofmanyyearsofteachingimageprocessingandfuzzylogic
taught by the authors for undergraduate courses. Most of the material used is also
the result offertile interactions with the students whose case studies contributed a
lot in the Java implementation of algorithms and methods. The volume has been
conceived as a gentle introduction to fuzzy logic approaches useful in image pro-
cessing tasks.
First we describe image processing algorithms based on fuzzy logic under a
methodological point of view. Then, we provide some practical applications
without passing over the important formal details. We tried to identify the most
important works that researchers have done in the area offuzzy image processing,
and we described and illustrated them through Java examples that the interested
readers can easily follow.
The book covers both theoretical and practical applications offuzzy techniques
in image processing. Accordingly, the chapters have been grouped into two parts:
Fundamentals of Fuzzy Image Processing and Applications to Image Processing.
In the first part, we explain how image processing can take advantage offuzzy
logic, giving basic theoretical aspects of both fuzzy logic and image processing
through five chapters. Chapter 1 is devoted to the basics of image representation
usingJava.Chapter2dealswithlow-levelimageprocessing.InChap.3thereader
willfindthebasicconceptsoffuzzylogic,startingfromfuzzysettheoryuptofuzzy
systems. Chapter 4 discusses the issue of vagueness in digital images, that is the
motivationofusingfuzzytechniquestoprocessimages.Finally,Chap.5introduces
the Java language and its use for image processing.
Inthesecondpart,wepresentfourchapterscoveringdifferentimageprocessing
tasks, namely color contrast enhancement, image segmentation, morphological
analysis,andimagethresholding.Foreachtaskanexampleofpracticalapplication
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viii Preface
is described. Some examples are presented in the medical domain, using light
microscopeimages providedbythe DipartimentodiEndocrinologiaedOncologia
Molecolare e Clinica of the University “Federico II” of Naples, Italy. Lastly, the
appendixprovidessomeJavacodeexamplesthattheusercaneasilyrunwhichwill
help create a concrete feeling of the potential fuzzy image processing.
We believe that this volume will provide a state-of-art coverage of various
aspects related to fuzzy image processing and show the potential of fuzzy tech-
niques in solving image processing problems. We hope this book will serve as a
reference for scientists and students in this area, as well as a means to stimulate
some new ideas for researchers.
We are grateful to a number of people from academic circles as well as from
domestic environments who have contributed to the writing of this book in many
differentways.Inparticular,wethankallthemembersoftheCILab(Computational
IntelligenceLaboratory)oftheDepartmentofInformaticsattheUniversityofBari
“Aldo Moro” for giving answers to our questions at the right time. We thank
Menina Di Gennaro for reading the first draft of some chapters and giving helpful
suggestions at the early stages of the work. Our special thanks go to Mara Basile
and Vito Corsini who gave their contribution to the research on morphology and
segmentation applied to the medical domain. We also thank our Ph.D. student
Przemyslaw Gorecky for his contribution on document analysis by fuzzy approa-
ches.Finally,wethankourstudentsAntonioVergaro,FrancescoTangari,Gabriella
Casalino, Marco Lucarelli, and Massimo Minervini for developing some Java
examples cited in this book. The contribution of everyone is truly appreciated.
Bari, Italy Laura Caponetti
June 2016 Giovanna Castellano
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Contents
Part I Fundamentals of Fuzzy Image Processing
1 Image Representation Using Java ... .... .... .... .... ..... .... 3
1.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 3
1.2 Gray-Level Images... ..... .... .... .... .... .... ..... .... 4
1.3 Color Models... .... ..... .... .... .... .... .... ..... .... 5
1.4 Color Image Representation Using Java.... .... .... ..... .... 10
References.. .... .... .... ..... .... .... .... .... .... ..... .... 13
2 Low-Level Image Processing ... .... .... .... .... .... ..... .... 15
2.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 15
2.2 Contrast Enhancement ..... .... .... .... .... .... ..... .... 18
2.2.1 Gray-Level Transformation.... .... .... .... ..... .... 20
2.2.2 Thresholding.. ..... .... .... .... .... .... ..... .... 27
2.2.3 Histogram Transformation. .... .... .... .... ..... .... 27
2.3 Image Smoothing.... ..... .... .... .... .... .... ..... .... 28
2.4 Edge Detection.. .... ..... .... .... .... .... .... ..... .... 30
2.4.1 Canny Operator..... .... .... .... .... .... ..... .... 34
2.4.2 Optimization-Based Operators.. .... .... .... ..... .... 36
References.. .... .... .... ..... .... .... .... .... .... ..... .... 37
3 Basics of Fuzzy Logic .... ..... .... .... .... .... .... ..... .... 39
3.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 39
3.2 Fuzzy Set Theory.... ..... .... .... .... .... .... ..... .... 40
3.3 Fuzzy Rule-Based Systems.. .... .... .... .... .... ..... .... 41
3.3.1 Fuzzification .. ..... .... .... .... .... .... ..... .... 43
3.3.2 Fuzzy Rule Base and Inference Engine... .... ..... .... 43
3.3.3 Defuzzification ..... .... .... .... .... .... ..... .... 44
3.4 Fuzzy Models... .... ..... .... .... .... .... .... ..... .... 45
3.4.1 Design of Fuzzy Rule-Based Systems.... .... ..... .... 46
3.4.2 Neuro-Fuzzy Models. .... .... .... .... .... ..... .... 48
References.. .... .... .... ..... .... .... .... .... .... ..... .... 51
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4 Fuzzy Image Processing .. ..... .... .... .... .... .... ..... .... 53
4.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 53
4.2 Image Fuzzification .. ..... .... .... .... .... .... ..... .... 55
4.2.1 Fuzzy Image.. ..... .... .... .... .... .... ..... .... 56
4.3 Image Defuzzification. ..... .... .... .... .... .... ..... .... 59
4.4 Fuzziness Measures .. ..... .... .... .... .... .... ..... .... 60
References.. .... .... .... ..... .... .... .... .... .... ..... .... 62
5 Java for Image Processing ..... .... .... .... .... .... ..... .... 65
5.1 Basic Concepts.. .... ..... .... .... .... .... .... ..... .... 65
5.2 Java for Image Processing .. .... .... .... .... .... ..... .... 68
5.3 Applet. .... .... .... ..... .... .... .... .... .... ..... .... 71
5.4 ImageJ .... .... .... ..... .... .... .... .... .... ..... .... 74
5.4.1 Macros .. .... ..... .... .... .... .... .... ..... .... 75
5.4.2 Plugins .. .... ..... .... .... .... .... .... ..... .... 75
5.5 Fuzzy Systems in Java..... .... .... .... .... .... ..... .... 76
References.. .... .... .... ..... .... .... .... .... .... ..... .... 81
Part II Application to Image Processing
6 Color Contrast Enhancement... .... .... .... .... .... ..... .... 85
6.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 85
6.2 Multichannel Image Processing .. .... .... .... .... ..... .... 86
6.3 Fuzzy Techniques for Color Enhancement.. .... .... ..... .... 86
6.4 A Fuzzy Rule-Based System for Color Enhancement.. ..... .... 87
6.5 Example: Natural Image Enhancement. .... .... .... ..... .... 88
References.. .... .... .... ..... .... .... .... .... .... ..... .... 91
7 Image Segmentation . .... ..... .... .... .... .... .... ..... .... 93
7.1 Introduction .... .... ..... .... .... .... .... .... ..... .... 93
7.2 The Segmentation Problem.. .... .... .... .... .... ..... .... 94
7.3 Methods for Segmentation .. .... .... .... .... .... ..... .... 95
7.3.1 Crisp Clustering .... .... .... .... .... .... ..... .... 96
7.3.2 Fuzzy Clustering.... .... .... .... .... .... ..... .... 97
7.3.3 Spatial Fuzzy Clustering.. .... .... .... .... ..... .... 98
7.4 Example: Color Segmentation ... .... .... .... .... ..... .... 99
7.5 Example: Texture Segmentation.. .... .... .... .... ..... .... 101
References.. .... .... .... ..... .... .... .... .... .... ..... .... 104
8 Morphological Analysis... ..... .... .... .... .... .... ..... .... 107
8.1 Mathematical Morphology .. .... .... .... .... .... ..... .... 107
8.1.1 Morphological Operators.. .... .... .... .... ..... .... 108
8.2 Fuzzy Morphology... ..... .... .... .... .... .... ..... .... 110
8.3 Example: Biological Image Segmentation... .... .... ..... .... 113
References.. .... .... .... ..... .... .... .... .... .... ..... .... 118
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