Fuzzy transforms for image processing and data analysis : core concepts, processes and applications

목차 (Table of Contents)

• CONTENTS
• 1 Fuzzy Transform Concepts = 1
• 1.1 Fuzzy Sets and Fuzzy Relations = 1
• 1.2 Generalized Fuzzy Partitions and Fuzzy Partitions Under Ruspini Condition = 8
• 1.3 Uniform Fuzzy Partition and H-Uniform Generalized Fuzzy Partition = 9
• 1.4 Direct and Inverse Fuzzy Transform = 11
• 1.5 Discrete Fuzzy Transform and Sufficient Density Concept = 12
• References = 14
• 2 Multi-dimensional and High Degree Fuzzy Transform = 15
• 2.1 Fuzzy Transform in Two Variables = 15
• 2.2 Multi-dimensional Fuzzy Transform = 16
• 2.3 Sufficient Density in Multi-Dimensional Fuzzy Transforms = 18
• 2.4 High Degree Fuzzy Transform = 20
• 2.5 F-Fuzzy Transform = 23
• References = 25
• 3 Fuzzy Transform for Image and Video Compression = 27
• 3.1 Coding and Decoding Images by Using Bi-Dimensional F-Transforms = 27
• 3.2 Image Compression with Block Decompositions = 29
• 3.3 High Degree Fuzzy Transforms for Coding／Decoding Images = 32
• 3.4 Color Image Compression in the YUV Space = 36
• 3.5 Multilevel Fuzzy Transform Image Compression = 37
• 3.6 Fuzzy Transform-Based Methods for Coding／Decoding Videos = 41
• References = 47
• 4 Fuzzy Transform Technique for Image Autofocus = 49
• 4.1 Passive Image Autofocus Techniques = 49
• 4.2 Passive Image Autofocus: Contrast Detection Measures = 50
• 4.3 Direct Fuzzy Transforms Applied for Passive Image Autofocus = 54
• References = 59
• 5 Fuzzy Transform for Image Fusion and Edge Detection = 61
• 5.1 Image Fusion Concept = 61
• 5.2 Image Decomposition via F-Transforms = 62
• 5.3 F-Transform Image Fusion Algorithms : The CA, SA, and ESA Algorithms = 65
• 5.4 The CCA Algorithm = 68
• 5.5 Edge Detection Concept = 73
• 5.6 F1 -Transform Method for Edge Detection = 74
• References = 78
• 6 Fuzzy Transform for Image Segmentation = 81
• 6.1 Image Segmentation Concept = 81
• 6.2 Image Thresholding—Fuzzy Entropy Maximization = 83
• 6.3 Fuzzy Transform Method for Image Thresholding = 86
• 6.4 Partitive Clustering Image Segmentation Algorithms = 89
• 6.5 Extensions of FCM for Image Segmentation = 92
• 6.6 F-Transform FGFCM Algorithm for Image Segmentation = 95
• References = 101
• 7 Fuzzy Transforms for Image Watermarking and Image Autofocus = 103
• 7.1 Image Watermarking Approaches : The Fragile Block-Wise Image Watermarking = 103
• 7.2 Image Tamper Detection = 105
• 7.3 Fuzzy Transform Method in Image Watermarking = 107
• 7.4 Fuzzy Transform Image Watermarking via Fuzzy Bilinear Equations = 112
• References = 120
• 8 Fuzzy Transform for Data Analysis = 123
• 8.1 Multi-dimensional Fuzzy Transform Applied in Data Analysis = 123
• 8.2 The Inverse Multi-dimensional Fuzzy Transform for Assessing Functional Dependencies in the Data = 125
• 8.3 The Problem of the Sufficient Density of Data Points with Respect to the Fuzzy Partition = 126
• 8.4 Fuzzy Transform Method for the Analysis of Numerical Attribute Dependencies in Datasets = 129
• 8.5 Fuzzy Transform Method for Mining Association in the Data = 133
• References = 136
• 9 Fuzzy Transforms in Prevision Analysis = 137
• 9.1 Time Series Forecasting = 137
• 9.2 One-Dimensional Direct F-Transforms in Time Series Analysis = 138
• 9.3 Fuzzy Forecasting Analysis : The Wang and Mendel Method = 141
• 9.4 Multi-dimensional F-Transform for Forecasting in Data Analysis = 145
• References = 152
• 10 Fuzzy Transforms Applied in Seasonal Time Series Analysis = 153
• 10.1 Seasonal Time Series = 153
• 10.2 F-Transform Technique to Remove Seasonal Components and Noise from Time Series = 155
• 10.3 Seasonal Time Series Fuzzy Transform Forecasting = 157
• 10.4 F1 -Transform for Seasonal Time Series Forecasting = 164
• References = 171
• 11 Fuzzy Transform for Data Classification = 173
• 11.1 Machine Learning Data Classification : Underfitting and Overfitting = 173
• 11.2 K-Folds Cross-Validation Techniques = 177
• 11.3 Multi-dimensional F-Transform for Data Classification = 179
• 11.4 K-Folds Cross-validation Applied to a Multi-dimensional F-Transform Classifier = 181
• 11.5 The MFC Algorithm = 183
• References = 190
• 12 Fuzzy Transform for Analyzing Massive Datasets = 193
• 12.1 Massive Data Definition and Concepts = 193
• 12.2 Massive Data Regression Analysis = 195
• References = 210
• Bibliography = 213
• Index = 215