Contents i SYLLABUS UNIT - I CHAPTER - 1 : INTRODUCTION TO DIGITAL IMAGE PROCESSING Introduction, Origins of Digital Image Processing, Applications of Digital Image Processing, Fundamental Steps, Components, Elements of Visual Perception, Light and the Electromagnetic Spectrum, Image Sensing and Acquistion, Image Sampling and Quantization, Some Basic Relationships between Pixels. CHAPTER - 2 : INTENSITY TRANSFORMATIONS TIONS Some Basic Intensity Transformation Functions, Histogram Processing. UNIT - II CHAPTER - 3 : SPATIAL FILTERING Fundamentals of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters. CHAPTER - 4 : FILTERING IN THE FREQUENCY DOMAIN Preliminary Concepts, Sampling and the Fourier Transform of Sampled Functions, Fourier Transform and the Frequency Domain, The Discrete Fourier Transform (DFT) of One Variable, Extension to Function of Two Variables, Image Smoothing using Frequency Domain Filters, Image Sharpening Using Frequency Domain Filters. UNIT - III CHAPTER - 5 : IMAGE RESTORA ORATION A Model of the Image Degradation/Restoration Process, Noise Models, Restoration in the Presence of Noise Only-Spatial Filtering, Inverse Filtering, Minimum Mean Square Error (Wiener) Filtering, Least Squares Filtering. CHAPTER - 6 : COLOR IMAGE PROCESSING Color Fundamentals, Color Models, Pseudo Color Image Processing, Basics of Full- Color Image Processing. CHAPTER - 7 : MORPHOLOGICAL IMAGE PROCESSING Preliminaries, Erosion and Dilation, Opening and Closing Functions.
ii Contents UNIT - IV CHAPTER - 8 : IMAGE COMPRESSION Fundamentals, Fidelity Criteria Image Compression Models, Lossless Compression, Lossy Compression, Image Compression Standards. UNIT - V CHAPTER - 9 : IMAGE SEGMENTATION TION Fundamentals, Point, Line and Edge Detection, Segmentation by Thresholding, Region- Based Segmentation, Segmentation Using Watershed Algorithm. CHAPTER - 10 : REPRESENTATION TION AND DESCRIPTION Representation, Some Simple Descriptors, Shape Numbers, Fourier Descriptors. CHAPTER - 11 : OBJECT RECOGNITION Patterns and Pattern Classes, Matching, Minimum Distance Classifier, Correlation.
Contents iii digital image processing FOR b.e. (o.u) IV year i semester (COMMON TO CSE AND IT) CONTENTS UNIT - I [CH. H. - 1] ] [INTRODUCTION TO DIGITAL IMAGE PROCESSING]... 1.1-1.46 1.1 INTRODUCTION... 1.2 1.2 ORIGINS OF DIGITAL AL IMAGE GE PROCESSING... 1.4 1.3 APPLICATIONS OF DIGITAL AL IMAGE GE PROCESSING... 1.4 1.4 FUNDAMENT AMENTAL AL STEPS IN DIGITAL AL IMAGE GE PROCESSING... 1.6 1.5 COMPONENTS OF DIGITAL AL IMAGE GE PROCESSING SYSTEM... 1.8 1.6 ELEMENTS OF VISUAL PERCEPTION... 1.9 1.6.1 Structure of the Human Eye... 1.10 1.6.2 Image Formation in the Eye... 1.12 1.6.3 Brightness Adaptation and Discrimination... 1.13 1.6.3.1 Contrast Sensitivity... 1.14 1.6.3.2 Brightness Adaptation... 1.15 1.6.3.3 Acquity and Contour (Mach Bands)... 1.17 1.6.3.4 Simultaneous Contrast... 1.17 1.6.3.5 Integration... 1.18
iv Contents 1.7 LIGHT AND THE ELECTROMAGNETIC SPECTRUM... 1.19 1.8 IMAGE SENSING AND ACQUISITION... 1.20 1.8.1 Image Acquisition using Sensors... 1.21 1.8.1.1 Image Acquisition Using Single Image Sensor... 1.21 1.8.1.2 Image Acquisition using Sensor Strips... 1.22 1.8.1.3 Image Acquisition Using Sensor Arrays... 1.23 1.8.2 A Simple Image Formation Model... 1.24 1.9 IMAGE SAMPLING AND QUANTIZA ANTIZATION TION... 1.26 1.9.1 Basic Concepts in Sampling and Quantization... 1.26 1.9.2 Representing Digital Images... 1.28 1.9.3 Spatial and Gray-Level Resolution... 1.29 1.9.3.1 Differences between Gray-Level Image and Binary Image... 1.34 1.9.4 Aliasing and Moire Patterns... 1.34 1.9.5 Zooming and Shrinking of Digital Images... 1.36 1.10 SOME BASIC RELATIONSHIPS BETWEEN PIXELS... 1.39 1.10.1 Neighbors of a Pixel... 1.39 1.10.2 Adjacency... 1.40 1.10.3 Connectivity,, Region and Boundary... 1.41 1.10.3.1 Connectivity... 1.41 1.10.3.2 Region... 1.41 1.10.3.3 Boundary... 1.41 1.10.4 Distance Measures... 1.41 1.10.5 Image Operations on a Pixel Basis... 1.45 1.10.6 Linear and Non-Linear Operations... 1.45
Contents v UNIT - I [CH. H. - 2] ] [INTENSITY TRANSFORMATIONS]... 1.47-1.78 2.1 BASICS OF INTENSITY TRANSFORMATION TION (GREY LEVEL TRANSFORMATION) TION)... 1.48 2.2 SOME BASIC INTENSITY TRANSFORMATION TION FUNCTIONS... 1.51 2.2.1 Image Negatives... 1.52 2.2.2 Log Transformations... 1.52 2.2.3 Power ower-l -Law Transformation ransformation... 1.53 2.2.3.1 Gamma Correction... 1.54 2.2.4 Piecewise iecewise-linear Transformation Functions... 1.55 2.2.4.1 Contrast Stretching... 1.55 2.2.4.2 Intensity Grey-Level Slicing... 1.56 2.2.4.3 Bit-Plane Slicing... 1.57 2.3 HISTOGRAM PROCESSING... 1.58 2.3.1 Histogram Equalization (or) Histogram Linearization... 1.62 2.3.1.1 Histogram Equalization for Continuous Functions... 1.62 2.3.1.2 Histogram Equalization for Discrete Values... 1.64 2.3.1.3 Advantages of Histogram Equalization... 1.65 2.3.2 Histogram Matching (Specification)... 1.70 2.3.2.1 Development of Direct Histogram Specification for Continuous Functions... 1.71 2.3.2.2 Development of Direct Histogram Specification for Discrete Functions... 1.72 2.3.2.3 Implementation... 1.73 2.3.3 Local Enhancement Technique... 1.76
vi Contents UNIT - II [CH.. - 3] ] [SP SPATIAL FILTERING TERING]... 2.1-2.12 3.1 FUNDAMENT AMENTALS ALS OF SPATIAL FILTERING... 2.2 3.2 SMOOTHING SPATIAL FILTERS... 2.4 3.2.1 Smoothing by Linear Filters... 2.4 3.2.2 Smoothing by Non-linear Filters... 2.6 3.3 SHARPENING SPATIAL FILTERS... 2.6 3.3.1 Use of Second Order Derivative for Enhancement- the Laplacian... 2.7 3.3.2 Use of First Order Derivative for Enhancement- the Gradient... 2.9 UNIT - II [CH. - 4] ] [FILTERING IN THE FREQUENCY DOMAIN]... 2.13-2.42 4.1 INTRODUCTION... 2.14 4.2 PRELIMINARY CONCEPTS... 2.14 4.2.1 Complex Number... 2.14 4.2.2 Fourier Series... 2.15 4.2.3 Impulses and their Shifting Property... 2.15 4.2.4 Fourier Transform of Functions of One Continuous Variable... 2.16 4.2.5 Convolution... 2.18 4.3 SAMPLING AND THE FOURIER TRANSFORM OF SAMPLED FUNCTIONS... 2.19 4.3.1 Sampling... 2.19 4.3.2 Fourier Transform of Sampled Functions... 2.21 4.3.3 Types of Sampling... 2.21 4.3.4 Sampling Theorem... 2.23
Contents vii 4.4 THE FOURIER TRANSFORM AND THE FREQUENCY DOMAIN... 2.23 4.4.1 The Discrete Fourier Transform (DFT) of One Variable... 2.23 4.4.2 Extension to Function of Two Variables... 2.24 4.4.3 Filtering in the Frequency Domain... 2.24 4.5 IMAGE SMOOTHING USING FREQUENCY DOMAIN FILTERS... 2.26 4.5.1 Ideal Low ow-p -Pass Filter (ILPF)... 2.26 4.5.2 Butterworth Low ow-p -Pass Filter (BLPF)... 2.30 4.5.3 Gaussian Low ow-p -Pass Filter (GLPF)... 2.31 4.6 IMAGE GE SHARPENING USING FREQUENCY DOMAIN FILTERS TERS... 2.35 4.6.1 Ideal High-Pass ass Filter (IHPF)... 2.35 4.6.2 Butterworth High-Pass ass Filter (BHPF)... 2.37 4.6.3 Gaussian High-Pass ass Filter (GHPF)... 2.38 UNIT - III [CH. - 5] ] [IMAGE RESTORATION]... 3.1-3.24 5.1 INTRODUCTION... 3.2 5.2 A MODEL OF THE IMAGE GE DEGRADATION/REST TION/RESTORA ORATION PROCESS... 3.2 5.3 NOISE MODELS... 3.6 5.3.1 Gaussian Noise... 3.6 5.3.2 Rayleigh Noise... 3.7 5.3.3 Erlang Noise (Gamma Noise)... 3.8 5.3.4 Exponential Noise... 3.8 5.3.5 Uniform Noise... 3.9 5.3.6 Impulse (Salt and Pepper) Noise... 3.10
viii Contents 5.4 RESTORA ORATION IN THE PRESENCE OF NOISE ONLY-SP SPATIAL FILTERING TERING... 3.11 5.4.1 Mean Filters... 3.12 5.4.2 Order-Statistics Filters... 3.14 5.4.3 Adaptive Filters... 3.15 5.5 IMAGE RESTORA ORATION TECHNIQUES... 3.17 5.5.1 Constrained Method... 3.17 5.5.1.1 Inverse Filtering... 3.18 5.5.1.1.1 Pseudo-Inverse Filtering... 3.19 5.5.2 Unconstrained Method... 3.19 5.5.2.1 Least Mean Square Filter (or) Wiener Filter... 3.20 5.5.2.1.1 Drawbacks of Wiener Filters... 3.21 5.5.2.2 Constrained Least Square Filter... 3.21 5.5.2.3 Comparison of Least Mean Squares Approach and Constrained Least Squares Restoration... 3.23 UNIT - III [CH. - 6] ] [COLOR IMAGE PROCESSING]... 3.25-3.40 6.1 INTRODUCTION... 3.26 6.2 COLOR OR FUNDAMENT AMENTALS ALS... 3.26 6.3 COLOR MODELS... 3.30 6.3.1 The RGB Color Model... 3.31 6.3.2 The CMY and CMYK Color Model... 3.33 6.3.3 The HSI Color Model... 3.34 6.4 PSEUDO COLOR IMAGE PROCESSING... 3.37 6.4.1 Intensity Slicing... 3.38 6.4.2 Gray Level to Color Transformation... 3.39 6.5 BASICS OF FULL-COLOR IMAGE PROCESSING... 3.39
Contents ix UNIT - III [CH. H. - 7] ] [MORPHOLOGICAL IMAGE PROCESSING]... 3.41-3.52 7.1 INTRODUCTION... 3.42 7.2 PRELIMINARIES... 3.42 7.2.1 Some Basic Concepts from Set Theory... 3.42 7.2.2 Logical Operations Involving Binary Images... 3.44 7.3 DILATION AND EROSION... 3.46 7.3.1 Dilation... 3.46 7.3.2 Erosion... 3.46 7.4 A SIMPLE PRACTICAL FORMULAE FOR IMPLEMENTING DILATION AND EROSION... 3.48 7.5 OPENING AND CLOSING OPERATIONS... 3.51 7.5.1 Opening Operation... 3.51 7.5.2 Closing Operation... 3.52 7.5.3 Properties of Opening and Closing Operations... 3.52 UNIT - IV [CH. - 8] ] [IMAGE COMPRESSION]... 4.1-4.78 8.1 INTRODUCTION... 4.2 8.2 FUNDAMENT AMENTALS ALS... 4.3 8.2.1 Coding Redundancy... 4.4 8.2.2 Interpixel Redundancy... 4.7 8.2.3 Psychovisual Redundancy... 4.8 8.3 FIDELITY CRITERIA... 4.10 8.3.1 Objective Fidelity Criteria... 4.10 8.3.2 Subjective Fidelity Criteria... 4.11
x Contents 8.4 IMAGE COMPRESSION SYSTEM MODEL... 4.12 8.4.1 Source Encoder and Decoder... 4.12 8.4.2 Channel Encoder and Decoder... 4.13 8.5 LOSSLESS (OR) ERROR-FREE COMPRESSION... 4.14 8.5.1 Variable ariable-l -Length Coding... 4.15 8.5.1.1 Huffman Coding... 4.16 8.5.1.1.1 Coding Procedure... 4.16 8.5.1.1.2 Advantages of Huffman Coding... 4.18 8.5.1.1.3 Disadvantages of Huffman Coding... 4.19 8.5.1.2 Arithmetic Coding... 4.19 8.5.1.2.1 Features of Arithmetic Coding... 4.19 8.5.1.2.2 Coding Procedure... 4.20 8.5.1.2.3 Limitations of Arithmetic Coding... 4.22 8.5.1.3 Comparison of Arithmetic Coding and Huffman Coding... 4.22 8.5.2 Golomb Coding... 4.22 8.5.3 LZW Coding... 4.25 8.5.3.1 Coding Procedure... 4.25 8.5.3.2 Advantages of LZW Coding... 4.26 8.5.3.3 Disadvantages of LZW Coding... 4.26 8.5.4 Bit-Plane Coding... 4.26 8.5.4.1 Bit-Plane Decomposition... 4.27 8.5.4.2 Compression of Bit-Planes... 4.28
Contents xi 8.5.4.2.1 Constant Area Coding (CAC)... 4.28 8.5.4.2.2 One-Dimensional Run-Length Coding... 4.29 8.5.4.2.3 Two wo-dimensional Run-L un-length Coding... 4.29 8.5.4.2.4 Contour Tracing and Coding... 4.31 8.5.5 Lossless Predictive Coding... 4.32 8.6 LOSSY COMPRESSION... 4.45 8.6.1 Lossy Predictive Coding... 4.46 8.6.2 Transform Coding... 4.47 8.6.2.1 Transform Coding System... 4.47 8.6.2.1.1 Transform Selection... 4.49 8.6.2.1.2 Sub-Image Size Selection... 4.54 8.6.2.1.3 Bit Allocation... 4.54 8.6.3 Wavelet Coding... 4.57 8.6.4 Comparison Among Transform Coding and Predictive Coding... 4.58 8.7 COMPARISON OF LOSSY AND LOSSLESS COMPRESSIONS... 4.60 8.8 IMAGE GE COMPRESSION STAND ANDARDS ARDS... 4.60 8.8.1 Joint Picture Expert Group (JPEG)... 4.60 8.8.2 Moving Picture Export Group (MPEG)... 4.69 8.8.2.1 MPEG-1... 4.69 8.8.2.2 MPEG-2... 4.73 8.8.3 Graphic Interchange Format Compression (GIF)... 4.76 8.9 IMAGE FORMATS AND CONTAINERS... 4.77
xii Contents UNIT - V [CH. H. - 9] ] [IMAGE SEGMENTATION]... 5.1-5.34 9.1 INTRODUCTION... 5.2 9.2 DETECTION OF DISCONTINUITIES... 5.3 9.2.1 Point Detection... 5.4 9.2.2 Line Detection... 5.5 9.2.3 Edge Detection... 5.6 9.2.3.1 Gradient Operators... 5.7 9.2.3.2 Laplacian Operators... 5.11 9.3 SEGMENTATION TION BY Y THRESHOLDING... 5.11 9.3.1 Foundation... 5.12 9.3.2 The Role of Illumination... 5.13 9.3.3 Basic Global Thresholding... 5.15 9.3.4 Basic Adaptive Thresholding... 5.16 9.3.5 Optimal Thresholding... 5.17 9.3.6 Local Thresholding... 5.20 9.3.7 Otsus Method... 5.21 9.4 REGION-BASED SEGMENTATION TION... 5.25 9.4.1 Basic Rules for Segmentation... 5.25 9.4.2 Region Growing... 5.26 9.4.3 Region Splitting and Merging... 5.28
Contents xiii 9.5 SEGMENTATION TION USING WATERSHED ALGORITHM... 5.29 9.5.1 Block Diagram of Watershed-Based Segmentation... 5.30 9.5.2 Watershed Algorithms... 5.30 9.5.2.1 Flooding Based Watershed Algorithms... 5.30 9.5.2.2 Rainfalling Watershed Algorithms... 5.33 UNIT - V [CH. H. - 10] ] [REPRESENTATION AND DESCRIPTION]... 5.35-5.50 10.1 INTRODUCTION... 5.36 10.2 REPRESENTATION TION... 5.36 10.2.1 Chain Codes... 5.36 10.2.2 Polygonal Approximation... 5.39 10.2.3 Signatures... 5.41 10.2.4 Boundary Segments... 5.42 10.2.5 Skeletons... 5.43 10.3 BOUNDAR ARY DESCRIPTORS ORS... 5.45 10.3.1 Simple Descriptors... 5.46 10.3.2 Shape Numbers... 5.46 10.3.3 Fourier Descriptors... 5.48
xiv Contents UNIT - V [CH. H. - 11] ] [OBJECT RECOGNITION]... 5.51-5.60 11.1 INTRODUCTION... 5.52 11.2 PATTERNS AND PATTERNS CLASSES... 5.52 11.3 RECOGNITION BASED ON DECISION-THEORETIC METHODS... 5.56 11.3.1 Matching... 5.57 11.3.1.1 Minimum Distance Classifier... 5.57 11.3.1.2 Matching by Correlation... 5.59