Fuzzy image processing and applications with MATLAB /
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| Author / Creator: | Chaira, Tamalika. |
|---|---|
| Imprint: | Boca Raton, FL : CRC Press/Taylor & Francis, c2010. |
| Description: | xv, 213 p. : ill. (some col.) ; 25 cm. |
| Language: | English |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/7921835 |
Table of Contents:
- Preface
- Authors
- 1. Fuzzy Subsets and Operations
- 1.1. Introduction
- 1.2. Concept of Fuzzy Subsets and Membership Function
- 1.2.1. Membership Function
- 1.3. Linguistic Hedges
- 1.4. Operations on Fuzzy Sets
- 1.5. Fuzzy Relations
- 1.5.1. Composition of Two Fuzzy Relations
- 1.5.2. Fuzzy Binary Relation
- 1.5.3. Transitive Closure of Fuzzy Binary Relation
- 1.6. Summary
- References
- 2. Image Processing in an Imprecise Environment
- 2.1. Introduction
- 2.2. Image as a Fuzzy Set
- 2.3. Fuzzy Image Processing
- 2.3.1. Foundations of Image Processing
- 2.3.1.1. Fuzzy Geometry
- 2.3.1.2. Measures of Fuzziness/Information
- 2.3.1.3. Rule-Based Systems
- 2.3.1.4. Fuzzy Clustering
- 2.3.1.5. Fuzzy Mathematical Morphology
- 2.3.1.6. Fuzzy Grammars
- 2.4. Some Applications of Fuzzy Set Theory in Image Processing
- 2.5. Summary
- References
- 3. Fuzzy Similarity Measure, Measure of Fuzziness, and Entropy
- 3.1. Introduction
- 3.2. Fuzzy Similarity and Distance Measures
- 3.2.1. Examples of Fuzzy Distance Measures
- 3.2.2. Fuzzy Divergence
- 3.3. Examples of Similarity Measures
- 3.3.1. Measure Based on Tversky's Model
- 3.3.2. Similarity of Fuzzy Sets Based on Distance
- 3.4. Measures of Fuzziness
- 3.4.1. Index of Fuzziness
- 3.4.2. Index of Nonfuzziness
- 3.4.3. Yager's Measure
- 3.5. Fuzzy Entropy
- 3.5.1. Logarithmic Entropy
- 3.5.2. Shannon Fuzzy Entropy
- 3.5.3. Total Entropy
- 3.5.4. Hybrid Entropy
- 3.6. Geometry of Fuzzy Subsets
- 3.7. Summary
- References
- 4. Fuzzy Image Preprocessing
- 4.1. Introduction
- 4.2. Contrast Enhancement
- 4.3. Fuzzy Image Contrast Enhancement
- 4.3.1. Contrast Improvement Using an Intensification Operator
- 4.3.2. Contrast Improvement Using Fuzzy Histogram Hyperbolization
- 4.3.3. Contrast Enhancement Using Fuzzy IF-THEN Rules
- 4.3.4. Contrast Improvement Using a Fuzzy Expected Value
- 4.3.5. Locally Adaptive Contrast Enhancement
- 4.4. Filters
- 4.5. Fuzzy Filters
- 4.6. Summary
- References
- 5. Thresholding Detection in Fuzzy Images
- 5.1. Introduction
- 5.2. Threshold Detection Methods
- 5.3. Types of Thresholding
- 5.3.1. Global Thresholding
- 5.3.2. Locally Adaptive Thresholding
- 5.3.3. Iterative Thresholding
- 5.3.4. Optimal Thresholding
- 5.3.5. Multispectral Thresholding
- 5.4. Thresholding Methods
- 5.5. Types of Fuzzy Methods
- 5.5.1. Gamma Membership Function
- 5.5.1.1. Fuzzy Divergence
- 5.5.1.2. Index of Fuzziness
- 5.5.1.3. Fuzzy Similarity Measure
- 5.6. Application of Thresholding
- 5.7. Summary
- References
- 6. Fuzzy Match-Based Region Extraction
- 6.1. Match-Based Region Extraction
- 6.2. Back Projection Algorithm
- 6.2.1. Swain and Ballard's Back Projection Algorithm
- 6.2.2. Quadratic Confidence Back Projection
- 6.2.3. Local Histogramming
- 6.2.4. Binary Set Back Projection
- 6.2.5. Single Element Quadratic Back Projection
- 6.3. Fuzzy Region Extraction Methods
- 6.3.1. Fuzzy Similarity Measures
- 6.3.2. Fuzzy Measures in Region Extraction
- 6.4. Summary
- References
- 7. Fuzzy Edge Detection
- 7.1. Introduction
- 7.2. Methods for Edge Detection
- 7.2.1. Thresholding-Based Methods
- 7.2.2. Boundary Method
- 7.2.3. Hough Transform Method
- 7.3. Fuzzy Methods
- 7.3.1. Fuzzy Sobel Edge Detector
- 7.3.2. Entropy-Based Fuzzy Edge Detection
- 7.3.3. Fuzzy Template Based Edge Detector
- 7.4. Summary
- References
- 8. Fuzzy Content-Based Image Retrieval
- 8.1. Introduction
- 8.2. Color Spaces
- 8.3. Content-Based Color Image Retrieval
- 8.3.1. Global-Based Approach
- 8.3.2. Partition-Based Approach
- 8.3.3. Regional-Based Approach
- 8.4. Image Retrieval Model
- 8.5. Fuzzy-Based Image Retrieval Methods
- 8.5.1. Fuzzy Similarity-Based Retrieval Model
- 8.5.2. Color Histogram-Based Retrieval
- 8.5.3. Smoothed Histogram-Based Retrieval
- 8.5.4. Fuzzy Similarity/Tversky's Measure-Based Retrieval Method
- 8.5.4.1. Fuzzy Similarity Measures
- 8.6. Summary
- References
- 9. Fuzzy Methods in Pattern Classification
- 9.1. Introduction
- 9.2. Decision Theoretic Pattern Classification Techniques
- 9.2.1. Preliminaries of Unsupervised Classification
- 9.3. Why a Fuzzy Classifier
- 9.3.1. Limitations of Statistical Classifiers
- 9.4. Fuzzy Set Theoretic Approach to Pattern Classification
- 9.5. Fuzzy Supervised Learning Algorithm
- 9.6. Fuzzy Partition
- 9.6.1. Pattern Classification Using a Fuzzy Similarity Measure
- 9.6.2. Fuzzy Similitude and Partitioning
- 9.7. Fuzzy Unsupervised Pattern Classification
- 9.8. Summary
- References
- 10. Application of Fuzzy Set Theory in Remote Sensing
- 10.1. Introduction
- 10.2. Why Fuzzy Techniques in Remote Sensing
- 10.3. About the Remotely Sensed Data
- 10.4. Classification of Remotely Sensed Data
- 10.5. Fuzzy Sets in Remote Sensing Data Analysis
- 10.6. Background Work in Neuro Fuzzy Computing in Remote Sensing
- 10.7. Background Work on Fuzzy Sets in Remote Sensing
- 10.8. Segmentation of Remote Sensing Images
- 10.9. Fuzzy Multilayer Perceptron
- 10.9.1. Fusion of Fuzzy Logic with Neural Networks
- 10.9.2. Fuzzy MLP with Back-Propagation Learning
- 10.9.3. Fuzzy Back-Propagation Classifier Architecture
- 10.10. Fuzzy Counter-Propagation Network
- 10.11. Fuzzy CPN for Classification of Remotely Sensed Data
- 10.11.1. General Description of the Test Scenes
- 10.11.2. Experimental Results
- 10.12. Summary
- References
- 11. MATLAB® Programs
- 11.1. Introduction
- 11.2. MATLAB Examples
- Problems
- Index
