Advanced Digital Image Processing and Its Applications in Big Data

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