دانلود کتاب Advanced Digital Image Processing and Its Applications in Big Data

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Cover Half Title Title Page Copyright Page Table of Content Preface Acknowledgments Authors Part I: Concept and Background of Image Processing, Techniques, and Big Data Chapter 1: Introduction to Advanced Digital Image Processing 1.1 Introduction 1.2 Categorization of Digital Images 1.2.1 Binary Image 1.2.2 Black and White Image 1.2.3 8-Bit Color Format 1.2.4 16 Color Format 1.2.5 24-Bit Format 1.3 Phases of Digital Image Processing 1.3.1 Acquisition of an Image 1.3.2 Image Enhancement References Chapter 2: Different Techniques Used for Image Processing 2.1 Introduction 2.1.1 Acquisition of an Image 2.1.2 Image Pre-Processing 2.1.2.1 Image Enhancement 2.1.2.2 Image Analysis 2.1.2.3 Image Compression 2.1.2.4 Edge Detection 2.1.2.5 Segmentation 2.1.2.6 Image Representation References Chapter 3: Role and Support of Image Processing in Big Data 3.1 Introduction 3.2 Big Data Mathematical Analysis Theories 3.2.1 Independent and Identical Distribution Theory (IID) 3.2.2 Set Theory 3.3 Characteristics of Big Data 3.4 Different Techniques of Big Data Analytics 3.4.1 Ensemble Analysis 3.4.2 Association Analysis 3.4.3 High-Dimensional Analysis 3.4.4 Deep Analysis 3.4.5 Precision Analysis 3.4.6 Divide and Conquer Analysis 3.4.7 Perspective Analysis 3.5 Steps of Big Data Processing 3.5.1 Data Collection 3.5.2 Data Storage and Management 3.5.3 Data Filtering and Extraction 3.5.4 Data Cleaning and Validation 3.5.5 Data Analytics 3.5.6 Data Visualization 3.6 Importance of Big Data in Image Processing 3.7 Hadoop 3.8 Parts of Hadoop Architecture 3.8.1 HDFS 3.8.2 Map Reduce 3.9 Working of HADOOP architecture 3.10 Image Processing with Big Data Analytics 3.11 Image preprocessing References Part II: Advanced Image Processing Technical Phases for Big Data Analysis Chapter 4: Advanced Image Segmentation Techniques Used for Big Data 4.1 Introduction 4.2 Classification of Image Segmentation Techniques 4.2.1 Region-based Segmentation 4.2.1.1 Threshold Segmentation 4.2.1.2 Regional Growth Segmentation 4.2.1.3 Region Splitting and Merging Methods 4.2.2 Edge Detection Segmentation 4.2.2.1 Sobel Operator 4.2.2.2 Laplacian Operator 4.2.3 Clustering-Based Segmentation 4.2.3.1 Hard Clustering 4.2.3.2 Soft Clustering 4.2.3.3 K-Means Clustering Technique 4.2.3.4 Fuzzy C-Means Clustering Technique 4.2.4 Segmentation Based on Weakly Supervised Learning in CNN 4.2.4.1 Comparative Study of Image Segmentation Techniques 4.3 Discussion References Chapter 5: Advance Object Detection and Clustering Techniques Used for Big Data 5.1 Introduction 5.2 Clustering 5.3 Differences between Clustering and Classification 5.4 Distance Measure 5.4.1 Euclidean Distance 5.4.2 Minkowski Metric 5.4.3 Manhattan Metric 5.5 Clustering Algorithms 5.5.1 Partitioning-Based Clustering 5.5.1.1 K-Means Clustering 5.5.2 Hierarchical Clustering 5.5.3 Model-Based Clustering 5.5.4 Density-Based Clustering 5.5.5 Fuzzy Clustering 5.5.6 Grid-Based Clustering 5.5.7 Exclusive Clustering 5.5.8 Overlapping Clustering Other Clustering Methods References Chapter 6: Advanced Image Compression Techniques Used for Big Data 6.1 Introduction 6.2 An Overview of the Compression Process 6.2.1 Concept of Image Compression 6.3 Related work of Image Compression Methods 6.4 Image Compression Techniques 6.4.1 Lossless Compression 6.4.2 Lossy Compression Techniques 6.4.3 Hybrid Compression Techniques 6.4.4 Some Advanced Image Compression Techniques 6.4.4.1 Vector Quantization (VQ) 6.5 Comparison of Various Compression Algorithms 6.5.1 Performance Parameters of Compression Techniques 6.5.1.1 Peak Signal-to-Noise Ratio 6.5.1.2 Compression Ratio 6.5.1.3 Mean Square Error 6.5.1.4 Structural Similarity Index 6.5.1.5 Bits per Pixel 6.5.1.6 Signal-to-Noise Ratio 6.5.1.7 Percent Rate of Distortion 6.5.1.8 Correlation Coefficient 6.5.1.9 Structural Content 6.6 Applications of Compression Techniques 6.6.1 Satellite Images 6.6.2 Broadcast Television 6.6.3 Genetic Images 6.6.4 Internet Telephony and Teleconferencing 6.6.5 Electronic Health Records 6.6.6 Computer Communication 6.6.7 Remote Sensing via Satellites References Part III: Various Application of Image Processing Chapter 7: Application of Image Processing and Data in Remote Sensing 7.1 Introduction 7.2 Remote Sensing References Chapter 8: Application of Image Processing and Data Science in Medical Science 8.1 Introduction 8.2 Ideal Dataset of Medical Imaging for Data Analysis 8.3 Fundamentals of Medical Image Processing 8.3.1 Steps of Image Processing 8.4 Problems with Medical Images 8.4.1 Heterogeneity of Images 8.4.2 Unknown Delineation of Objects 8.4.3 Robustness of Algorithms 8.4.4 Noise Occurrence in Image 8.4.4.1 Speckle Noise 8.5 Categories of Medical Image Data formation 8.5.1 Image Acquisition 8.5.1.1 X-ray Medical Images 8.5.1.2 Tomography Images 8.5.1.3 CT Images 8.5.1.4 Radiography Images 8.5.1.5 MRI 8.5.1.6 Ultrasound Images 8.5.1.7 Thermo Graphic Images 8.5.1.8 Molecular Imaging or Nuclear Medicine 8.5.1.8.1 PET 8.5.1.8.2 SPECT 8.5.2 Image Digitalization 8.5.2.1 Quantization 8.5.2.2 Spatial Sampling 8.5.3 Image Enhancement 8.5.3.1 Histogram Transforms 8.5.3.2 Phase of Registration 8.5.4 Image Data Visualization 8.5.5 Image Data analysis 8.5.5.1 Feature Extraction 8.5.5.2 Image Segmentation 8.5.5.3 Image Classification 8.5.6 Image Management 8.5.6.1 Archiving 8.5.6.2 Communication 8.5.6.3 Retrieval References Chapter 9: Application of Image Processing in Traffic Management and Analysis 9.1 Introduction 9.2 Smart Traffic Management Systems 9.2.1 Real-Time System 9.2.2 Data Analysis System 9.3 Review Work 9.4 Working of Real-Time Traffic Management References Chapter 10: Application of Image Processing and Data Science in Advancing Education Innovation 10.1 Introduction 10.2 Role of Image Processing in Education 10.3 Integrating Image Processing in Teaching and Learning in Schools 10.4 Role of Image-Based Computerized Learning in Education 10.5 Important Roles of Image Processing in Education 10.6 Assessing Creativity and Motivation in Image-Based Learning Systems 10.6.1 Building Character through Interactive Media 10.6.2 Image Processing 10.6.2.1 Image Acquisition 10.6.2.2 Image Enhancement 10.6.2.3 Image Restoration 10.6.2.4 Color Image Processing 10.6.2.5 Wavelets and Multiresolution Processing 10.6.2.6 Image Compression 10.6.2.7 Morphological Processing 10.6.2.8 Segmentation 10.6.2.9 Representation and Description 10.6.2.10 Object Recognition 10.6.2.11 Learning Content Mapping 10.7 Learners and Educators on the Image-Based Computerized Environment 10.7.1 Teaching Practices 10.7.2 Raising Learners Attainment 10.7.3 Inequalities Reduction among Learners 10.8 Discussion References Chapter 11: Application of Image Processing and Data Science in Advancing Agricultural Design 11.1 Introduction 11.2 Image Processing Techniques in Agriculture 11.2.1 Thermal Imaging Components of Thermal Imaging 11.2.2 Fluorescence Imaging 11.2.3 Hyperspectral Imaging 11.2.4 Photometric (RGB) Feature-Based Imaging 11.3 Application of Digital Image Processing with Data Science in Agriculture 11.3.1 Management of Crop 11.3.2 Identifying the Deficiencies of Nutrition in Plants 11.3.3 Inspection of Quality of Fruits along with Their Sorting and Grading 11.3.4 Estimation of Crop and Land and Tracking of Object 11.3.5 Identification of Diseases in Plants 11.3.6 Precision Farming 11.3.7 Weed Detection 11.4 Newer Techniques in the Agriculture Support System 11.4.1 Aeroponic System 11.4.2 Artificial Intelligence in Agriculture References Index