Cover -- Preface -- Acknowledgement -- About the Authors -- Contents -- Chapter 1: Introduction to Digital Image Processing -- 1.1 Introduction -- 1.2 Steps in Image Processing -- 1.3 Building Blocks of a Digital Image Processing System -- 1.3.1 Image Acquisition -- 1.3.2 Storage -- 1.3.3 Processing -- 1.3.4 Display and Communication Interface -- Summary -- Review Questions -- Chapter 2: Digital Image Representation -- 2.1 Introduction -- 2.2 Digital Image Representation -- 2.3 Sampling and Quantization -- 2.4 Basic Relationship Between Pixels -- 2.4.1 Neighbors and Connectivity -- 2.4.2 Distance Measure -- Summary -- Review Questions -- Chapter 3: Image Transforms -- 3.1 Introduction -- 3.2 Fourier Transform -- 3.3 Discrete Fourier Transform -- 3.4 Properties of Fourier Transform -- 3.4.1 Separability -- 3.4.2 Translation -- 3.4.3 Periodicity and Conjugate Symmetry -- 3.4.4 Rotation -- 3.4.5 Distributivity and Scaling -- 3.4.6 Average Value -- 3.4.7 Laplacian -- 3.4.8 Convolution and Correlation -- 3.5 Fast Fourier Transform -- 3.5.1 Fast Fourier Transform Algorithm -- 3.5.2 The Inverse FFT -- 3.6 Discrete Cosine Transform -- 3.6.1 Properties of Cosine Transform -- 3.7 Walsh Transform -- 3.8 Hadamard Transform -- 3.9 The Haar Transform -- 3.10 The Slant Transform -- 3.11 The Hotelling Transform -- Summary -- Review Questions -- Chapter 4: Image Enhancement -- 4.1 Introduction -- 4.2 Spatial Domain and Frequency Domain Approaches -- 4.2.1 Frequency Domain Techniques -- 4.3 Spatial Domain Techniques -- 4.3.1 Negative of an Image -- 4.3.2 Contrast Stretching -- 4.3.3 Gray Level Slicing -- 4.3.4 Bit Plane Slicing -- 4.3.5 Histogram and Histogram Equalization -- 4.3.6 Histogram Specifications -- 4.3.7 Local Enhancement Technique -- 4.3.8 Image Subtraction -- 4.3.9 Image Average -- 4.4 Spatial Filtering -- 4.4.1 Low-Pass Spatial Filters.

4.4.2 Median Filtering -- 4.4.3 High-Pass Spatial Filters -- 4.4.4 High-Boost Filter -- 4.4.5 Derivative Filters -- 4.5 Frequency Domain -- 4.5.1 Ideal Low-Pass Filter -- 4.5.2 Butterworth Low-Pass Filter -- 4.5.3 High-Pass Filter -- 4.5.4 Homomorphic Filtering -- 4.5.5 Pseudo Color Image -- 4.6 Gray Level to Color Transformation -- 4.6.1 Filter Approach for Color Coding -- Summary -- Review Questions -- Chapter 5: Image Compression -- 5.1 Introduction -- 5.2 Coding Redundancy -- 5.3 Inter-Pixel Redundancy -- 5.4 Psycho-Visual Redundancy -- 5.5 Image Compression Models -- 5.6 The Source Encoder and Decoder -- 5.7 The Channel Encoder and Decoder -- 5.8 Information Theory -- 5.8.1 Information -- 5.8.2 Entropy Coding -- 5.9 Classification -- 5.10 Huffman Coding -- 5.10.1 Arithmetic Coding -- 5.10.2 Lossless Predictive Coding -- 5.11 Lossy Compression Techniques -- 5.11.1 Lossy Predictive Compression Approach -- 5.11.2 Transform Coding -- 5.11.3 Subimage Selection -- 5.11.4 Coefficients Selection -- 5.12 Threshold Coding -- 5.13 Vector Quantization -- 5.14 Image Compression Standard (JPEG) -- 5.15 Image Compression Using Neural Networks -- 5.15.1 Multilayer Perceptron Network for Image Compression -- 5.15.2 Vector Quantization Using Neural Networks -- 5.15.3 Self-Organizing Feature Map -- Summary -- Review Questions -- Chapter 6: Image Segmentation -- 6.1 Introduction -- 6.2 Detection of Isolated Points -- 6.3 Line Detection -- 6.4 Edge Detection -- 6.4.1 Gradient Operators -- 6.4.2 Laplacian Operator -- 6.5 Edge Linking and Boundary Detection -- 6.5.1 Local Processing -- 6.5.2 Global Processing Using Graph Theoretic Approach -- 6.6 Region-Oriented Segmentation -- 6.6.1 Basic Rules for Segmentation -- 6.6.2 Region Growing by Pixel Aggregation -- 6.6.3 Region Splitting and Merging -- 6.7 Segmentation Using Threshold -- 6.7.1 Fundamental Concepts.

6.7.2 Optimal Thresholding -- 6.7.3 Threshold Selection Based on Boundary Characteristics -- 6.7.4 Use of Motion in Segmentation -- 6.8 Accumulative Difference Image -- Summary -- Review Questions -- Chapter 7: Image Restoration -- 7.1 Introduction -- 7.2 Degradation Model -- 7.3 Degradation Model for Continuous Functions -- 7.4 Discrete Degradation Model -- 7.5 Estimation of Degradation Function -- 7.6 Estimation by Experimentation -- 7.7 Estimation by Modeling -- 7.8 Inverse Filtering Approach -- 7.9 Least Mean Square Filter -- 7.10 Interactive Restoration -- 7.11 Constrained Least Squares Restoration -- 7.11.1 Geometric Transformations -- 7.11.2 Spatial Transformations -- 7.11.3 Gray Level Interpolation -- Summary -- Review Questions -- Chapter 8: Image Representation and Description -- 8.1 Introduction -- 8.2 Boundary Representation Using Chain Codes -- 8.3 Boundary Representation Using Line Segments -- 8.4 Boundary Representation Using Signature -- 8.5 Shape Number -- 8.6 Fourier Descriptors -- 8.7 Moments -- 8.8 Region Representation -- 8.8.1 Run-Length Codes -- 8.8.2 Quad Tree -- 8.8.3 Skeletons -- 8.9 Regional Descriptors -- 8.10 Topological Descriptors -- 8.11 Texture -- 8.11.1 Statistical Approach -- 8.11.2 Structural Approach -- 8.12 Relational Descriptors -- Summary -- Review Questions -- Chapter 9: Pattern Classification Methods -- 9.1 Introduction -- 9.2 Statistical Pattern Classification Methods -- 9.2.1 Supervised and Unsupervised Learning Methods -- 9.2.2 Parametric Approaches -- 9.2.3 Nonparametric Approaches -- 9.2.4 Deterministic Trainable Classification Algorithms -- 9.3 Artificial Intelligence Approach in Pattern Classification -- 9.4 ANN Approaches in Pattern Classification -- 9.4.1 Backpropagation Training Algorithm for MLP Classifier -- 9.4.2 Experimentation With MLP Classifier -- 9.4.3 Classification of Mechanical Components.

9.4.4 Prediction of Subsidence in Coal -- 9.4.5 Kohonen's Self-Organizing Map (SOM) Network -- 9.5 Supervised Feedforward Fuzzy Neural Network -- 9.5.1 Fuzzy Neuron -- 9.5.2 Structure of the Fuzzy Neural Classifier -- 9.5.3 Dynamically Organizing SFFNN Learning Algorithm -- 9.5.4 Analysis of the SFFNN Classifier -- 9.5.5 Experimental Results -- 9.5.6 Simulation -- 9.6 Syntactic Pattern Recognition -- 9.6.1 Formal Language Theory -- 9.7 Types of Grammar -- 9.8 Syntactic Recognition Problem Using Formal Language -- 9.9 Image Knowledge Base -- 9.9.1 Frames -- 9.9.2 Predicate Logic -- Summary -- Review Questions -- Illustrations -- Bibliography -- Index.

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.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.