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Fundamentals of Digital Image Processing

Book Description

Fundamentals of Digital Image Processing clearly discusses the five fundamental aspects of digital image processing namely, image enhancement, transformation, segmentation, compression and restoration. Presented in a simple and lucid manner, the book aims to provide the reader a sound and firm theoretical knowledge on digital image processing. It is supported by large number of colored illustrations.

Table of Contents

  1. Cover
  2. Title Page
  3. Contents
  4. About the Authors
  5. Dedication
  6. Preface
  7. Chapter 1 - Fundamentals of Digital Image Processing
    1. 1.1 - Introduction
    2. 1.2 - Steps in Image Processing
    3. 1.3 - Building Blocks of a Digital Image Processing System
      1. 1.3.1 - Image Acquisition
      2. 1.3.2 - Storage
      3. 1.3.3 - Processing
      4. 1.3.4 - Display and Communication Interface
  8. Chapter 2 - Digital Image Representation
    1. 2.1 - Introduction
    2. 2.2 - Digital Image Representation
    3. 2.3 - Sampling and Quantization
    4. 2.4 - Basic Relationship between Pixels
      1. 2.4.1 - Neighbors and Connectivity
      2. 2.4.2 - Distance Measure
  9. Chapter 3 - Image Transforms
    1. 3.1 - Introduction
    2. 3.2 - Fourier Transform
    3. 3.3 - Discrete Fourier Transform
    4. 3.4 - Properties of Fourier Transform
      1. 3.4.1 - Separability
      2. 3.4.2 - Translation
      3. 3.4.3 - Periodicity and Conjugate Symmetry
      4. 3.4.4 - Rotation
      5. 3.4.5 - Distributivity and Scaling
      6. 3.4.6 - Average Value
      7. 3.4.7 - Laplacian
      8. 3.4.8 - Convolution and Correlation
    5. 3.5 - Fast Fourier Transform
      1. 3.5.1 - Fast Fourier Transform Algorithm
      2. 3.5.2 - The Inverse FFT
    6. 3.6 - Discrete Cosine Transform
      1. 3.6.1 - Properties of Cosine Transform
    7. 3.7 - Walsh Transform
    8. 3.8 - Hadamard Transform
    9. 3.9 - The Haar Transform
    10. 3.10 - The Slant Transform
    11. 3.11 - The Hotelling Transform
  10. Chapter 4 - Image Enhancement
    1. 4.1 - Introduction
    2. 4.2 - Spatial Domain and Frequency Domain Approaches
      1. 4.2.1 - Frequency Domain Techniques
    3. 4.3 - Spatial Domain Techniques
      1. 4.3.1 - Negative of an Image
      2. 4.3.2 - Contrast Stretching
      3. 4.3.3 - Gray Level Slicing
      4. 4.3.4 - Bit Plane Slicing
      5. 4.3.5 - Histogram and Histogram Equalization
      6. 4.3.6 - Histogram Specifications
      7. 4.3.7 - Local Enhancement Technique
      8. 4.3.8 - Image Subtraction
      9. 4.3.9 - Image Average
    4. 4.4 - Spatial Filtering
      1. 4.4.1 - Low-Pass Spatial Filters
      2. 4.4.2 - Median Filtering
      3. 4.4.3 - High-Pass Spatial Filters
      4. 4.4.4 - High-Boost Filter
      5. 4.4.5 - Derivative Filters
    5. 4.5 - Frequency Domain
      1. 4.5.1 - Ideal Low-Pass Filter
      2. 4.5.2 - Butterworth Low-Pass Filter
      3. 4.5.3 - High-Pass Filter
      4. 4.5.4 - Homomorphic Filtering
      5. 4.5.5 - Pseudo Color Image
    6. 4.6 - Gray Level to Color Transformation
      1. 4.6.1 - Filter Approach for Color Coding
  11. Chapter 5 - Image Compression
    1. 5.1 - Introduction
    2. 5.2 - Coding Redundancy
    3. 5.3 - Inter-Pixel Redundancy
    4. 5.4 - Psycho-Visual Redundancy
    5. 5.5 - Image Compression Models
    6. 5.6 - The Source Encoder and Decoder
    7. 5.7 - The Channel Encoder and Decoder
    8. 5.8 - Information Theory
      1. 5.8.1 - Information
      2. 5.8.2 - Entropy Coding
    9. 5.9 - Classification
    10. 5.10 - Huffman Coding
      1. 5.10.1 - Arithmetic Coding
      2. 5.10.2 - Lossless Predictive Coding
    11. 5.11 - Lossy Compression Techniques
      1. 5.11.1 - Lossy Predictive Compression Approach
      2. 5.11.2 - Transform Coding
      3. 5.11.3 - Subimage Selection
      4. 5.11.4 - Coefficients Selection
    12. 5.12 - Threshold Coding
    13. 5.13 - Vector Quantization
      1. 5.13.1 - Searching Algorithms
    14. 5.14 - Image Compression Standard (JPEG)
    15. 5.15 - Image Compression Using Neural Networks
      1. 5.15.1 - Multilayer Perceptron Network for Image Compression
      2. 5.15.2 - Vector Quantization using Neural Networks
      3. 5.15.3 - Self-Organizing Feature Map
  12. Chapter 6 - Image Segmentation
    1. 6.1 - Introduction
    2. 6.2 - Detection of Isolated Points
    3. 6.3 - Line Detection
    4. 6.4 - Edge Detection
      1. 6.4.1 - Gradient Operators
      2. 6.4.2 - Laplacian Operator
    5. 6.5 - Edge Linking and Boundary Detection
      1. 6.5.1 - Local Processing
      2. 6.5.2 - Global Processing using Graph Theoretic Approach
    6. 6.6 - Region-Oriented Segmentation
      1. 6.6.1 - Basic Rules for Segmentation
      2. 6.6.2 - Region Growing by Pixel Aggregation
      3. 6.6.3 - Region Splitting and Merging
    7. 6.7 - Segmentation using Threshold
      1. 6.7.1 - Fundamental Concepts
      2. 6.7.2 - Optimal Thresholding
      3. 6.7.3 - Threshold Selection Based on Boundary Characteristics
      4. 6.7.4 - Use of Motion in Segmentation
    8. 6.8 - Accumulative Difference Image
  13. Chapter 7 - Image Restoration
    1. 7.1 - Introduction
    2. 7.2 - Degradation Model
    3. 7.3 - Degradation Model for Continuous Functions
    4. 7.4 - Discrete Degradation Model
    5. 7.5 - Estimation of Degradation Function
    6. 7.6 - Estimation by Experimentation
    7. 7.7 - Estimation by Modeling
    8. 7.8 - Inverse Filtering Approach
    9. 7.9 - Least Mean Square Filter
    10. 7.10 - Interactive Restoration
    11. 7.11 - Constrained Least Squares Restoration
      1. 7.11.1 - Geometric Transformations
      2. 7.11.2 - Spatial Transformations
      3. 7.11.3 - Gray Level Interpolation
  14. Chapter 8 - Image Representation and Description
    1. 8.1 - Introduction
    2. 8.2 - Boundary Representation using Chain Codes
    3. 8.3 - Boundary Representation using Line Segments
    4. 8.4 - Boundary Representation using Signature
    5. 8.5 - Shape Number
    6. 8.6 - Fourier Descriptors
    7. 8.7 - Moments
    8. 8.8 - Region Representation
      1. 8.8.1 - Run-Length Codes
      2. 8.8.2 - Quad Tree
      3. 8.8.3 - Skeletons
    9. 8.9 - Regional Descriptors
    10. 8.10 - Topological Descriptors
    11. 8.11 - Texture
      1. 8.11.1 - Statistical Approach
      2. 8.11.2 - Structural Approach
    12. 8.12 - Relational Descriptors
  15. Chapter 9 - Pattern Classification Methods
    1. 9.1 - Introduction
    2. 9.2 - Statistical Pattern Classification Methods
      1. 9.2.1 - Supervised and Unsupervised Learning Methods
      2. 9.2.2 - Parametric Approaches
      3. 9.2.3 - Nonparametric Approaches
      4. 9.2.4 - Deterministic Trainable Classification Algorithms
    3. 9.3 - Artificial Intelligence Approach in Pattern Classification
    4. 9.4 - ANN Approaches in Pattern Classification
      1. 9.4.1 - Backpropagation Training Algorithm for MLP Classifier
      2. 9.4.2 - Experimentation with MLP Classifier
      3. 9.4.3 - Classification of Mechanical Components
      4. 9.4.4 - Prediction of Subsidence in Coal
      5. 9.4.5 - Kohonen's Self-Organizing Map (SOM) Network
    5. 9.5 - Supervised Feedforward Fuzzy Neural Network
      1. 9.5.1 - Fuzzy Neuron
      2. 9.5.2 - Structure of the Fuzzy Neural Classifier
      3. 9.5.3 - Dynamically Organizing SFFNN Learning Algorithm
      4. 9.5.4 - Analysis of the SFFNN Classifier
      5. 9.5.5 - Experimental Results
      6. 9.5.6 - Simulation
    6. 9.6 - Syntactic Pattern Recognition
      1. 9.6.1 - Formal Language Theory
    7. 9.7 - Types of Grammar
    8. 9.8 - Syntactic Recognition Problem using Formal Language
    9. 9.9 - Image Knowledge Base
      1. 9.9.1 - Frames
      2. 9.9.2 - Predicate Logic
  16. Illustrations
  17. Bibliography
  18. Acknowledgements
  19. Copyright