Image processing-from basics to advanced applications
Learn how to master image processing and compression with this outstanding state-of-the-art reference. From fundamentals to sophisticated applications, Image Processing: Principles and Applications covers multiple topics and provides a fresh perspective on future directions and innovations in the field, including:
- Image transformation techniques, including wavelet transformation and developments
- Image enhancement and restoration, including noise modeling and filtering
- Segmentation schemes, and classification and recognition of objects
- Texture and shape analysis techniques
- Fuzzy set theoretical approaches in image processing, neural networks, etc.
- Content-based image retrieval and image mining
- Biomedical image analysis and interpretation, including biometric algorithms such as face recognition and signature verification
- Remotely sensed images and their applications
- Principles and applications of dynamic scene analysis and moving object detection and tracking
- Fundamentals of image compression, including the JPEG standard and the new JPEG2000 standard
Additional features include problems and solutions with each chapter to help you apply the theory and techniques, as well as bibliographies for researching specialized topics. With its extensive use of examples and illustrative figures, this is a superior title for students and practitioners in computer science, wireless and multimedia communications, and engineering.
Table of Contents:
Preface
1. Introduction
1.1 Fundamentals of Image Processing
1.2 Applications of Image Processing
1.3 Human Visual Perception
1.4 Components of an Image Processing System
1.5 Organization of this book.
1.6 How is this book different?
1.7 Summary
2. Image Formation and Representation
2.1 Introduction
2.2 Image formation
2.3 Sampling and Quantization
2.4 Binary Image
2.5 Connected component labeling
2.6 Image fled formats
2.7 Some Important Notes
2.8 Types of Image Processing Operations
2.9 Summary
3. Color and Color Imagery
3.1 Introduction
3.2 Perception of Colors and Spectral sensitivity of human eyes
3.3 Color Space Quantization and the Just Noticeable Difference (JND)
3.4 Color Space and Transformation
3.5 Color Interpolation or Demosaicing
3.6 Summary
4. Image Transformation
4.1 Introduction
4.2 Fourier Transforms
4.3 Discrete Cosine Transform
4.4 Walsh Hadamard Transform (WHT)
4.5 Karhaunen-Loeve Transform or Principal Component Analysis
4.6 Summary
5. Discrete Wavelet Transform
5.1 Introduction
5.2 Wavelet Transforms
5.3 Extension to Two-Dimensional Signals
5.4 Lifting Implementation of the DWT
5.5 Why Do We Care About Lifting?
5.6 Applications Areas in Image Processing
5.7 Summary
6. Image Enhancement and Restoration.
6.1 Introduction
6.2 Distinction between image enhancement and restoration
6.3 Spatial Image Enhancement Techniques
6.4 Noise Filtering
6.5 Image Enhancement - Frequency Domain approach
6.6 Noise Modeling
6.7 Image Restoration
6.8 Summary
7. Image Segmentation
7.1 Preliminaries
7.2 Edge, Line, and Point Detection
7.3 Edge Detector
7.4 Image Thresholding Techniques
7.5 Color Image Segmentation
7.6 Waterfall algorithm for segmentation
7.7 Document Image segmentation
7.8 Summary
8. Recognition of Image Patterns
8.1 Introduction
8.2 Bayesian Decision Theory
8.3 Non-parametric Classification
8.4 Unsupervised Classification Strategies - clustering
8.5 K-means Clustering Algorithm
8.6 Primitive selection Strategies
8.7 High Dimensional Pattern Grammars
8.8 Formal Linguistic model
8.9 Automata Theory
8.10 Structural recognition of imprecise Patterns
8.11 Symbolic Projection Method
8.12 Classification using Neural Networks
8.13 Crisp Neural Networks For Scene Classification
8.14 Architecture of Back propagation network
8.15 Research Direction
8.16 Summary
9. Texture and Shape Analysis
9.1 Introduction
9.2 Drawbacks of Grey Level Co-occurrence Matrix (GLCM)
9.3 Spatial Relationship
9.4 Weak Texture Measures
9.5 Strong Texture Measures and Generalized Co-occurrence
9.6 Texture Spectrum
9.7 Texture Classification using Fractals
9.8 Fractals in Texture Classification
9.9 Structural Methods
9.10 Shape Analysis
9.11 Dominant points in Shape Description
9.12 Polygonal Approximation for Shape Analysis
9.13 Automatic recognition of Guns
9.14 Active Contour modeling
9.15 Gestalt Theory of Perception
9.16 Summary
10. Fuzzy Set Theory in Image Processing
10.1 Introduction to Fuzzy Set Theory
10.2 Why Fuzzy Image?
10.3 Introduction to Fuzzy Set Theory
10.4 Preliminaries and Background
10.5 Image as a Fuzzy Set
10.6 Fuzzy Methods of Contrast Enhancement
10.7 Determination of the Fuzzication Parameters
10.8 Results
10.9 Fuzzy Spatial Filter for Noise Removal
10.10 Smoothing Algorithm
10.11 Fuzzy Histogram Modeling
10.12 Image Segmentation using Fuzzy Methods
10.13 Fuzzy C Means Algorithm
10.14 Fuzzy Approaches to Pattern Recognition
10.15 Fusion of fuzzy logic with neural networks
10.16 Summary
11. Image Mining and Content Based Image Retrieval
11.1 Introduction
11.2 Representation of images in a CBIR System
11.3 Model of a image retrieval system
11.4 Image Mining
11.5 Video Mining
11.6 Summary
12. Biometric And Biomedical Image Processing
12.1 Introduction
12.2 Face Recognition
12.3 Face Recognition Using Eigenfaces.
12.4 Signature Verification
12.5 Preprocessing of Signature Patterns
12.6 Biomedical Image Analysis
12.7 X - ray Image Analysis
12.8 Uses of X-ray images
12.9 Biomedical Imaging Techniques
12.10 Dental x-ray image analysis
12.11 Mammogram Image Analysis
12.12 Research direction
12.13 Summary
13. Remotely Sensed Multispectral Scene Analysis
13.1 Introduction
13.2 Satellite sensors and imageries
13.3 Features of Multispectral Images
13.4 Spectral reflectance of various earth objects
13.5 Scene Classification Strategies
13.6 Spectral classification - A knowledge Based Approach
13.7 Spatial Reasoning
13.8 Fuzzy Set Theoretic Approaches in Remote Sensing
13.9 Summary
14. Dynamic Scene Analysis: Moving Object Detection and Tracking
14.1 Introduction
14.2 Problem Definition
14.3 Adaptive Background Modelling
14.4 Connected Component Labeling
14.5 Shadow Detection
14.6 Principles of object Tracking
14.7 Model of Tracker System
14.8 Condensation Algorithm
14.9 Particle Filter Based object Tracking
14.10 Summary
15. Introduction to Image Compression
15.1 Introduction
15.2 Information Theory Concepts
15.3 Classification of Compression algorithms
15.4 Source Coding Algorithms
15.5 Huffman Coding
15.6 Arithmetic Coding
15.7 Summary
16. JPEG: Still Image Compression Standard
16.1 Introduction
16.2 The JPEG Lossless Coding Algorithm
16.3 Baseline JPEG Compression
16.4 Summary
17. JPEG2000 Standard
17.1 Introduction
17.2 Why JPEG2000?
17.3 Parts of the JPEG2000 Standard
17.4 Overview of the JPEG2000 Part 1 Encoding System
17.5 Image Preprocessing
17.6 Compression
17.7 Tier-2 Coding and Bitstream Formation
17.8 Summary
18. Coding Algorithms in JPEG2000
18.1 Introduction
18.2 Partitioning Data for Coding
18.3 Tier-1 Coding in JPEG2000
18.4 Tier-2 Coding in JPEG2000
18.5 Summary
References
Index
About the Authors
