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E-raamat: Fuzzy Image Processing and Applications with MATLAB [Taylor & Francis e-raamat]

(Bengal Engg. and Science University, Shibpur),
  • Formaat: 238 pages, 3 Tables, black and white; 14 Illustrations, color; 197 Illustrations, black and white
  • Ilmumisaeg: 24-Nov-2009
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781315218328
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  • Taylor & Francis e-raamat
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Fuzzy Image Processing and Applications with MATLABSuurem pilt
  • Formaat: 238 pages, 3 Tables, black and white; 14 Illustrations, color; 197 Illustrations, black and white
  • Ilmumisaeg: 24-Nov-2009
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781315218328
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781315218328
Chaira (Indian Institute of Technology) and Ray (Bengal Engineering and Science University) introduce the fundamentals of fuzzy subsets and their operations, define fuzzy similarity and distance measures, and describe image preprocessing methods. The graduate textbook walks through fuzzy methods for image thresholding, region extraction, edge detection, image retrieval, pattern classification, and remote sensing. The final chapter contains MATLAB code for each image processing algorithm. Small black and white images and a few color images are provided. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)

In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge.

Fuzzy Image Processing and Applications with MATLAB® presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few.

Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation.

Minimize Processing Errors Using Dynamic Fuzzy Set Theory

This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecision—whether it is in the grey level of the image, geometry of an object, definition of an object’s edges or boundaries, or in knowledge representation, object recognition, or image interpretation.

The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.

Preface xi
Authors xv
1. Fuzzy Subsets and Operations
1
1.1 Introduction
1
1.2 Concept of Fuzzy Subsets and Membership Function
1
1.2.1 Membership Function
2
1.3 Linguistic Hedges
10
1.4 Operations on Fuzzy Sets
11
1.5 Fuzzy Relations
14
1.5.1 Composition of Two Fuzzy Relations
16
1.5.2 Fuzzy Binary Relation
17
1.5.3 Transitive Closure of Fuzzy Binary Relation
18
1.6 Summary
19
References
20
2. Image Processing in an Imprecise Environment
21
2.1 Introduction
21
2.2 Image as a Fuzzy Set
23
2.3 Fuzzy Image Processing
24
2.3.1 Foundations of Image Processing
24
2.3.1.1 Fuzzy Geometry
24
2.3.1.2 Measures of Fuzziness/Information
24
2.3.1.3 Rule-Based Systems
25
2.3.1.4 Fuzzy Clustering
25
2.3.1.5 Fuzzy Mathematical Morphology
25
2.3.1.6 Fuzzy Grammars
26
2.4 Some Applications of Fuzzy Set Theory in Image Processing
26
2.5 Summary
28
References
28
3. Fuzzy Similarity Measure, Measure of Fuzziness, and Entropy
31
3.1 Introduction
31
3.2 Fuzzy Similarity and Distance Measures
32
3.2.1 Examples of Fuzzy Distance Measures
33
3.2.2 Fuzzy Divergence
33
3.3 Examples of Similarity Measures
35
3.3.1 Measure Based on Tversky's Model
35
3.3.2 Similarity of Fuzzy Sets Based on Distance
37
3.4 Measures of Fuzziness
37
3.4.1 Index of Fuzziness
38
3.4.2 Index of Nonfuzziness
39
3.4.3 Yager's Measure
39
3.5 Fuzzy Entropy
40
3.5.1 Logarithmic Entropy
40
3.5.2 Shannon Fuzzy Entropy
40
3.5.3 Total Entropy
40
3.5.4 Hybrid Entropy
42
3.6 Geometry of Fuzzy Subsets
43
3.7 Summary
43
References
44
4. Fuzzy Image Preprocessing
45
4.1 Introduction
45
4.2 Contrast Enhancement
47
4.3 Fuzzy Image Contrast Enhancement
47
4.3.1 Contrast Improvement Using an Intensification Operator
49
4.3.2 Contrast Improvement Using Fuzzy Histogram Hyperbolization
52
4.3.3 Contrast Enhancement Using Fuzzy IF-THEN Rules
53
4.3.4 Contrast Improvement Using a Fuzzy Expected Value
54
4.3.5 Locally Adaptive Contrast Enhancement
55
4.4 Filters
56
4.5 Fuzzy Filters
58
4.6 Summary
63
References
63
5. Thresholding Detection in Fuzzy Images
67
5.1 Introduction
67
5.2 Threshold Detection Methods
68
5.3 Types of Thresholding
69
5.3.1 Global Thresholding
69
5.3.2 Locally Adaptive Thresholding
70
5.3.3 Iterative Thresholding
71
5.3.4 Optimal Thresholding
71
5.3.5 Multispectral Thresholding
72
5.4 Thresholding Methods
72
5.5 Types of Fuzzy Methods
74
5.5.1 Gamma Membership Function
79
5.5.1.1 Fuzzy Divergence
80
5.5.1.2 Index of Fuzziness
82
5.5.1.3 Fuzzy Similarity Measure
83
5.6 Application of Thresholding
87
5.7 Summary
89
References
91
6. Fuzzy Match-Based Region Extraction
93
6.1 Match-Based Region Extraction
93
6.2 Back Projection Algorithm
95
6.2.1 Swain and Ballard's Back Projection Algorithm
95
6.2.2 Quadratic Confidence Back Projection
96
6.2.3 Local Histogranuning
97
6.2.4 Binary Set Back Projection
97
6.2.5 Single Element Quadratic Back Projection
97
6.3 Fuzzy Region Extraction Methods
98
6.3.1 Fuzzy Similarity Measures
98
6.3.2 Fuzzy Measures in Region Extraction
100
6.4 Summary
107
References
107
7. Fuzzy Edge Detection
109
7.1 Introduction
109
7.2 Methods for Edge Detection
109
7.2.1 Thresholding-Based Methods
110
7.2.2 Boundary Method
111
7.2.3 Hough Transform Method
111
7.3 Fuzzy Methods
111
7.3.1 Fuzzy Sobel Edge Detector
112
7.3.2 Entropy-Based Fuzzy Edge Detection
113
7.3.3 Fuzzy Template Based Edge Detector
116
7.4 Summary
122
References
123
8. Fuzzy Content-Based Image Retrieval
125
8.1 Introduction
125
8.2 Color Spaces
126
8.3 Content-Based Color Image Retrieval
128
8.3.1 Global-Based Approach
128
8.3.2 Partition-Based Approach
129
8.3.3 Regional-Based Approach
130
8.4 Image Retrieval Model
130
8.5 Fuzzy-Based Image Retrieval Methods
131
8.5.1 Fuzzy SimilarityBased Retrieval Model
132
8.5.2 Color Histogram-Based Retrieval
134
8.5.3 Smoothed Histogram-Based Retrieval
134
8.5.4 Fuzzy Similarity/Tversky's Measure-Based Retrieval Method
136
8.5.4.1 Fuzzy Similarity Measures
137
8.6 Summary
142
References
142
9. Fuzzy Methods in Pattern Classification
145
9.1 Introduction
145
9.2 Decision Theoretic Pattern Classification Techniques
146
9.2.1 Preliminaries of Unsupervised Classification
148
9.3 Why a Fuzzy Classifier
151
9.3.1 Limitations of Statistical Classifiers
151
9.4 Fuzzy Set Theoretic Approach to Pattern Classification
152
9.5 Fuzzy Supervised Learning Algorithm
153
9.6 Fuzzy Partition
155
9.6.1 Pattern Classification Using a Fuzzy Similarity Measure
156
9.6.2 Fuzzy Similitude and Partitioning
156
9.7 Fuzzy Unsupervised Pattern Classification
161
9.8 Summary
163
References
163
10. Application of Fuzzy Set Theory in Remote Sensing 165
10.1 Introduction
165
10.2 Why Fuzzy Techniques in Remote Sensing
165
10.3 About the Remotely Sensed Data
166
10.4 Classification of Remotely Sensed Data
167
10.5 Fuzzy Sets in Remote Sensing Data Analysis
168
10.6 Background Work in Neuro Fuzzy Computing in Remote Sensing
169
10.7 Background Work on Fuzzy Sets in Remote Sensing
172
10.8 Segmentation of Remote Sensing Images
173
10.9 Fuzzy Multilayer Perception
175
10.9.1 Fusion of Fuzzy Logic with Neural Networks
176
10.9.2 Fuzzy MLP with Back-Propagation Learning
176
10.9.3 Fuzzy Back-Propagation Classifier Architecture
177
10.10 Fuzzy Counter-Propagation Network
178
10.11 Fuzzy CPN for Classification of Remotely Sensed Data
179
10.11.1 General Description of the Test Scenes
179
10.11.2 Experimental Results
181
10.12 Summary
182
References
183
11. MATLAB® Programs 185
11.1 Introduction
185
11.2 MATLAB Examples
187
Problems 201
Index 207
Tamalika Chaira received her bachelors degree in electronics and communication from Bihar Institute of Technology, Sindri, India; her masters degree in electronics and communication from BE College, Shibpur; and her Ph.D in image processing from the Indian Institute of Technology, Kharagpur, India, in 1993, 2000, and 2003, respectively. Her research interests include image processing, fuzzy logic, intuitionistic fuzzy logic, and medical information processing. She has the following achievements to her credit: 1 Intel U.S. patent, 10 papers in international journals, several book chapters, and several papers in international conferences. She is listed in the International Biographic Centre, Cambridge, United Kingdom, and Marquis Whos Who in Science and Engineering, United States. She is a reviewer of several IEEE journals. She is also a member of Soft Computing in Image Processing. Currently, she is a young scientist in the Department of Science and Technology working at the Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, India.

Professor Ajoy Kumar Ray is currently the vice-chancellor of Bengal Engineering and Science University, Howrah, India. He received his BE in electronics and telecommunication engineering from Bengal Engineering College, Sibpur, India, and his MTech and Ph.D from the Indian Institute of Technology (IIT), Kharagpur, India. Prior to this, he was the head of the School of Medical Science and Technology and a professor of electronics and electrical communication engineering at IIT Kharagpur. Professor Ray has successfully completed 17 research projects as principal investigator, sponsored by Intel Corporation, Texas Instruments, in addition to those funded by agencies such as Defense Research and Development Organization (DRDO), Department of Science and Technology (DST), the Department of Atomic Energy, and the Department of Information Technology, India. He was at the University of Southampton during 1989-1990 and was the head of the research division at Avisere, Inc., United States, during 2004-2005. He has coauthored four books published by international publishing houses. He is the coinventor of six U.S. patents filed jointly with Intel Corporation, as well as three patents filed jointly with Texas Instruments. He has coauthored more than 90 research papers in international journals and conferences. As secretary and chairman of the Nehru Museum of Science and Technology, Professor Ray has conceptualized and created several galleries on science and technology. His research interests include image processing, machine intelligence, soft computing, and molecular imaging in disease detection.
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