Advanced Digital Image Processing and Its Applications in Big Data - Advanced Digital Image Processing and Its Applications in Big Data -

I: Concept and Background of Image Processing, Techniques, and Big Data : Introduction to Advanced Digital Image Processing Introduction Categorization of Digital Images Binary Image Black and White Image -Bit Color Format Color Format -Bit Format Phases of Digital Image Processing Acquisition of an Image Image Enhancement References : Different Techniques Used for Image Processing Introduction Acquisition of an Image Image Pre-Processing Image Enhancement Image Analysis Image Compression Edge Detection Segmentation Image Representation References : Role and Support of Image Processing in Big Data Introduction Big Data Mathematical Analysis Theories Independent and Identical Distribution Theory (IID)Set Theory Characteristics of Big Data Different Techniques of Big Data Analytics Ensemble Analysis Association Analysis High-Dimensional Analysis Deep Analysis Precision Analysis Divide and Conquer Analysis Perspective Analysis Steps of Big Data Processing Data Collection Data Storage and Management Data Filtering and Extraction Data Cleaning and Validation Data Analytics Data Visualization Importance of Big Data in Image Processing Hadoop Parts of Hadoop Architecture HDFS Map Reduce Working of HADOOP architecture Image Processing with Big Data Analytics Image preprocessing References II: Advanced Image Processing Technical Phases for Big Data Analysis : Advanced Image Segmentation Techniques Used for Big Data Introduction Classification of Image Segmentation Techniques Region-based Segmentation Threshold Segmentation Regional Growth Segmentation Region Splitting and Merging Methods Edge Detection Segmentation Sobel Operator Laplacian Operator Clustering-Based Segmentation Hard Clustering Soft Clustering K-Means Clustering Technique Fuzzy C-Means Clustering Technique Segmentation Based on Weakly Supervised Learning in CNN Comparative Study of Image Segmentation Techniques Discussion References : Advance Object Detection and Clustering Techniques Used for Big Data Introduction Clustering Differences between Clustering and Classification Distance Measure Euclidean Distance Minkowski Metric Manhattan Metric Clustering Algorithms Partitioning-Based Clustering K-Means Clustering Hierarchical Clustering Model-Based Clustering Density-Based Clustering Fuzzy Clustering Grid-Based Clustering Exclusive Clustering Overlapping Clustering Other Clustering Methods References : Advanced Image Compression Techniques Used for Big Data Introduction An Overview of the Compression Process Concept of Image Compression Related work of Image Compression Methods Image Compression Techniques Lossless Compression Lossy Compression Techniques Hybrid Compression Techniques Some Advanced Image Compression Techniques Vector Quantization (VQ)Comparison of Various Compression Algorithms Performance Parameters of Compression Techniques Peak Signal-to-Noise Ratio Compression Ratio Mean Square Error Structural Similarity Index Bits per Pixel Signal-to-Noise Ratio Percent Rate of Distortion Correlation Coefficient Structural Content Applications of Compression Techniques Satellite Images Broadcast Television Genetic Images Internet Telephony and Teleconferencing Electronic Health Records Computer Communication Remote Sensing via Satellites References III: Various Application of Image Processing : Application of Image Processing and Data in Remote Sensing Introduction Remote Sensing References : Application of Image Processing and Data Science in Medical Science Introduction Ideal Dataset of Medical Imaging for Data Analysis Fundamentals of Medical Image Processing Steps of Image Processing Problems with Medical Images Heterogeneity of Images Unknown Delineation of Objects Robustness of Algorithms Noise Occurrence in Image Speckle Noise Categories of Medical Image Data formation Image Acquisition X-ray Medical Images Tomography Images CT Images Radiography Images MRI Ultrasound Images Thermo Graphic Images Molecular Imaging or Nuclear Medicine Image Digitalization Quantization Spatial Sampling Image Enhancement Histogram Transforms Phase of Registration Image Data Visualization Image Data analysis Feature Extraction Image Segmentation Image Classification Image Management Archiving Communication Retrieval References : Application of Image Processing in Traffic Management and Analysis Introduction Smart Traffic Management Systems Real-Time System Data Analysis System Review Work Working of Real-Time Traffic Management References : Application of Image Processing and Data Science in Advancing Education Innovation Introduction Role of Image Processing in Education Integrating Image Processing in Teaching and Learning in Schools Role of Image-Based Computerized Learning in Education Important Roles of Image Processing in Education Assessing Creativity and Motivation in Image-Based Learning Systems Building Character through Interactive Media Image Processing Image Acquisition Image Enhancement Image Restoration Color Image Processing Wavelets and Multiresolution Processing Image Compression Morphological Processing Segmentation Representation and Description Object Recognition Learning Content Mapping Learners and Educators on the Image-Based Computerized Environment Teaching Practices Raising Learners Attainment Inequalities Reduction among Learners Discussion References : Application of Image Processing and Data Science in Advancing Agricultural Design Introduction Image Processing Techniques in Agriculture Thermal Imaging Components of Thermal Imaging Fluorescence Imaging Hyperspectral Imaging Photometric (RGB) Feature-Based Imaging Application of Digital Image Processing with Data Science in Agriculture Management of Crop Identifying the Deficiencies of Nutrition in Plants Inspection of Quality of Fruits along with Their Sorting and Grading Estimation of Crop and Land and Tracking of Object Identification of Diseases in Plants Precision Farming Weed Detection Newer Techniques in the Agriculture Support System Aeroponic System Artificial Intelligence in Agriculture References