Image Processing and Intelligent Computing Systems

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Acknowledgement
Chapter 1: Digital Image Processing: Theory and Applications
	1.1 An Introduction to Image Processing
	1.2 Key Concepts of Image Processing
		1.2.1 What is Digital Image Processing?
		1.2.2 Image Matrix Representation
		1.2.3 Pixel
		1.2.4 Pixel Neighborhoods
		1.2.5 How Pixels Are Processed
		1.2.6 Image Types
	1.3 Fundamental Steps in Digital Image Processing
	1.4 Applications of Image Processing
		1.4.1 Noise
		1.4.2 Scrambling
		1.4.3 Forgery
		1.4.4 Medical
	1.5 Conclusions and Future Work
	References
Chapter 2: Content-Based Image Retrieval Using Texture Features
	2.1 Introduction
	2.2 The State of the Art
	2.3 Texture Features for CBIR
	2.4 The Proposed Method
	2.5 Experiment and Results
	2.6 Performance Evaluation
	2.7 Retrieval Results
	2.8 Performance Comparison
	2.9 Conclusion
	References
Chapter 3: Use of Computer Vision Techniques in Healthcare Using MRI Images
	3.1 Introduction
		3.1.1 Difficulties and Opportunities
		3.1.2 Obstacles in the Realm of Medical Imaging
	3.2 Analysis of Medical Images
		3.2.1 Typical Applications of AI in Medical Imaging Include the Following
	3.3 Computer In Healthcare, Computer Vision
		3.3.1 CV and AI in Health Imaging
	3.4 Applications of Computer Vision in Healthcare
	3.5 Critical Achievement Factor
	3.6 Discussion and Conclusions
	References
Chapter 4: Hierarchical Clustering Fuzzy Features Subset Classifier with Ant Colony Optimization for Lung Image Classification
	4.1 Introduction
	4.2 Literature Review
	4.3 System Design
	4.4 Result and Discussion
	4.5 Conclusion
	References
Chapter 5: Health-Mentor: A Personalized Health Monitoring System Using the Internet of Things and Blockchain Technologies
	5.1 Introduction
	5.2 Related Works
	5.3 IoT-based Health Monitoring
	5.4 Machine Learning-based Health Data Classification
	5.5 Blockchain-Based Health Data Transfer and Storage
	5.6 Summary of Existing Techniques
	5.7 Research Gap in the Existing Technique
	5.8 Objective of the Proposed Work
		5.8.1 Proposed Health-Mentor System
	5.9 IoT Data Collection
	5.10 Normal and Abnormal Data Classification
	5.11 Block Generation and Transfer
	5.12 Block Analysis and Recommendation System
	5.13 Experimental Results
	5.14 Machine Learning Algorithm-Based Normal and Abnormal Data Classification
	5.15 Block Construction and Transfer Analysis
	5.16 Block Analysis and Recommender System Analysis
	5.17 Conclusion and Future Work
	References
Chapter 6: Image Analysis Using Artificial Intelligence in Chemical Engineering Processes: Current Trends and Future Directions
	6.1 Introduction
	6.2 Artificial Intelligence in Practice
		6.2.1 The Impact on Academic Research
		6.2.2 Impact in Industrial Practice
	6.3 AI Principles
		6.3.1 Data-Driven Approach
		6.3.2 Knowledge-Based Approach
	6.4 Image Analysis Using AI
		6.4.1 Image Analysis in Process Systems Engineering
		6.4.2 Image Analysis in the Petroleum Industry
			6.4.2.1 Machine Learning in Upstream
		6.4.3 Image Analysis in Wastewater Treatment
	6.5 Real-Time Quality Monitoring System
	6.6 Catalyst Design Using Image Processing
	6.7 AI in Fault Detection and Diagnosis
	6.8 Goals and Scopes of Image Analysis Using AI in Practice
	6.9 Challenges of Image Analysis in Industry
	6.10 Recent Trends and Future Outlook
	6.11 Conclusion
	References
Chapter 7: Automatic Vehicle Number Plate Text Detection and Recognition Using MobileNet Architecture for a Single Shot Detection (SSD) Technique
	7.1 Problem Statement
	7.2 Objective of the Study
	7.3 Introduction
	7.4 Review of the Literature
	7.5 Methodology
	7.6 Data Collection
	7.7 Automatic Number Plate Detection Process
	7.8 Installing and Setup Python Libraries
	7.9 Download TF Model Pretrained Model Form TensorFlow Model Zoo and Install TFOD
	7.10 Getting Number Plates Data
	7.11 Training the Object Detection Model
	7.12 Detecting Plates from an Image
	7.13 Real-time Detection Using WebCam
	7.14 Applying OCR
	7.15 Results After Detection Process
	7.16 Results and Discussions
	7.17 Comparative Analysis
	7.18 Conclusion
	7.19 Future Work
	References
Chapter 8: Medical Image Compression Using a Radial Basic Function Neural Network: Towards Aiding the Teleradiology for Medical Data Storage and Transfer
	8.1 Introduction
	8.2 Methodology
		8.2.1 Data Acquisition
		8.2.2 Medical Image Compression/Decompression Using Neural Network Algorithms
	8.3 Results and Discussion
	8.4 Conclusion
	References
Chapter 9: Prospects of Wearable Inertial Sensors for Assessing Performance of Athletes Using Machine Learning Algorithms
	9.1 Introduction
	9.2 The State of the Art in Motion Sensing
		9.2.1 3-D Motion Capture System
		9.2.2 Wearable IMU Sensors
		9.2.3 Electrogoniometers
		9.2.4 Force Plate Mechanism
		9.2.5 Medical Imaging Techniques
	9.3 Wearable Inertial Sensors for Sports Biomechanics
	9.4 Machine Learning (ML) Algorithm for Precision Measurement
		9.4.1 Kalman Filter
		9.4.2 Extended Kalman Filter
		9.4.3 Extended Kalman Filter Algorithm
		9.4.4 Zero-Velocity (ZUPT) Update
		9.4.5 Cascaded Kalman Filter
		9.4.6 Quaternion Concept
	9.5 Conclusion
	References
Chapter 10: Long Short-Term Memory Neural Network, Bottleneck Distance, and Their Combination for Topological Facial Expression Recognition
	10.1 Introduction
	10.2 Some Mathematical Background
		10.2.1 A Brief Introduction to Homology Theory
		10.2.2 Barcodes and Persistence Diagrams
		10.2.3 Distance Functions
	10.3 A Methodology for Facial Expression Recognition
		10.3.1 Global View of the Proposed Design
		10.3.2 Barcode Extraction for Facial Expressions
		10.3.3 Facial Expression Classification
		10.3.4 Classification Based on the Bottleneck Distance
		10.3.5 Classification Based on LSTM
		10.3.6 Classification Based on a Combination of Bottleneck and LSTM
	10.4 Experiments and Results
		10.4.1 Data Collection
		10.4.2 Evaluation Standards
		10.4.3 Classification Results
			10.4.3.1 Classification Based on Bottleneck Distance
			10.4.3.2 Classification Based on LSTM Recurrent Neural Network
			10.4.3.3 Classification Using a Combination of Bottleneck and LSTM Classifiers
	10.5 Conclusion and Future Work
	Acknowledgments
	References
Chapter 11: A Comprehensive Assessment of Recent Advances in Cervical Cancer Detection for Automated Screening
	11.1 Introduction
		11.1.1 Cervical Cancer Monitoring and Detection Methods
	11.2 Manual Screening Procedure
		11.2.1 Cervical Cancer Screening and Diagnosis Procedures
	11.3 Applications of Artificial Intelligence in Cervical Cancer Early Screening
		11.3.1 Testing and Detection of HPV
		11.3.2 Cervical Cytology Examination
			11.3.2.1 Cervical Cell Segmentation
			11.3.2.2 Cervical Cell Classification
			11.3.2.3 AI Enhances Cervical Intraepithelial Lesion Screening Accuracy
	11.4 Applications of Artificial Intelligence in Cervical Cancer Diagnosis
		11.4.1 Colposcopy
			11.4.1.1 Artificial Intelligence Improves Image Classification
			11.4.1.2 Artificial Intelligence Aids in the Detection of High-Grade Cervical Lesions and Biopsy Guidance
		11.4.2 MRI of the Pelvis
			11.4.2.1 Cervical Cancer Lesions Segmentation
			11.4.2.2 Cervical Cancer Diagnosis LNM
	11.5 Future Directions and Limitations
	References
Chapter 12: A Comparative Performance Study of Feature Selection Techniques for the Detection of Parkinson’s Disease from Speech
	12.1 Introduction
	12.2 Proposed Methodology
	12.3 PD Features
	12.4 Feature Selection
	12.5 Fisher Score
	12.6 mRMR (minimum Redundancy Maximum Relevance)
	12.7 Chi-Square
	12.8 Classification
	12.9 Assessment of Feature Selection Methods
	12.10 Results and Interpretation
	12.11 Conclusion and Perspectives
	References
Chapter 13: Enhancing Leaf Disease Identification with GAN for a Limited Training Dataset
	13.1 Introduction
	13.2 Materials and Methods
		13.2.1 Dataset
		13.2.2 Method
			13.2.2.1 DCGAN
			13.2.2.2 StyleGAN 2
			13.2.2.3 The Fine-Tuning of CNN for Classification
	13.3 Experimental Setup
		13.3.1 GAN Training
		13.3.2 Generating Images
		13.3.3 Results and Discussions
	13.4 Conclusion
	Acknowledgments
	References
Chapter 14: A Vision-Based Segmentation Technique Using HSV and YCbCr Color Model
	14.1 Introduction
	14.2 Existing State-of-the-Art Gesture Recognition Systems
	14.3 Proposed System Overview
	14.4 Results
	14.5 Conclusion
	References
Chapter 15: Medical Anomaly Detection Using Human Action Recognition
	15.1 Introduction
	15.2 Related Work
		15.2.1 Keypoint Detection
		15.2.2 Anomaly Detection
	15.3 Technical Approach
		15.3.1 Key Points Detection
		15.3.2 Action Classification
		15.3.3 Working of the Model
		15.3.4 Optimizers and Training Process
	15.4 Dataset and Experimentation
	15.5 Conclusion
	References
Chapter 16: Architecture, Current Challenges, and Research Direction in Designing Optimized, IoT-Based Intelligent Healthcare Systems
	16.1 Introduction
		16.1.1 IoT Integrated with a Cloud Computing-Based Healthcare System Basically Processes in Four Steps as Follows
	16.2 Pros and Cons of IoT in Healthcare Intelligent System
		16.2.1 Advantages of a Cloud IoT-based Healthcare System
		16.2.2 Limitations of an IoT-based Intelligent Healthcare System
	16.3 Applications of IoT in Intelligent Healthcare Systems
	16.4 Current Challenges and Research Direction of IoT in an Intelligent Healthcare System
		16.4.1 Current Challenges and the Research Direction of IoT in an Intelligent Healthcare System
		16.4.2 The Research Background of IoT in an Intelligent Healthcare System
		16.4.3 Hardware and Software Startups that provide High-End Solutions for Current Healthcare Problems
	16.5 Conclusion
	References
Chapter 17: Wireless Body Area Networks (WBANs) – Design Issues and Security Challenges
	17.1 Wireless Body Area Network Introduction
	17.2 WBAN Architecture
	17.3 WBAN Security and Privacy Requirements
	17.4 Security Threats in Wireless Body Area Networks
		17.4.1 WBAN Current Measures for Data Security Which Are Important and Not to Be Ignored
	17.5 Future Implementation for an Efficient Wireless Body Area Network
		17.5.1 Types of Attacks
	17.6 Conclusion
	References
Chapter 18: Cloud of Things: A Survey on Critical Research Issues
	18.1 Introduction
		18.1.1 Delivery of Cloud services
	18.2 Integration Benefits of Cloud-IoT
		18.2.1 Benefits
		18.2.2 Applications of Cloud-IoT
	18.3 Research Issues
	18.4 Security Issues in Cloud-IoT
	18.5 Conclusion
	Acknowledgement
	References
Chapter 19: Evaluating Outdoor Environmental Impacts for Image Understanding and Preparation
	19.1 Introduction
	19.2 Related Works
		19.2.1 Applications that Do Not Consider the Impact of Rain, Shadow, Darkness, and Fog
		19.2.2 Other Applications
	19.3 Our Approach for Image Data Understanding and Preparation
		19.3.1 Image Data Understanding
			19.3.1.1 Image Data Gathering
			19.3.1.2 Verifying Image Data Quality
		19.3.2 Assessing the Consistency among the Quality Values of the Images Captured Under a Particular Environmental Impact
		19.3.3 Mapping Environmental Impact into JPEG Image Quality and Gaussian Noise Level
		19.3.4 Applying Consistency and JPEG Image Quality and Gaussian Noise Level for Image Data Preparation
	19.4 Experimental Method
		19.4.1 Datasets
	19.5 Results and Discussions
		19.5.1 Analysis of Image Quality
		19.5.2 Evaluating the Consistency Among the Quality Values for a Particular Impact Level
		19.5.3 Assessing the Impacts in Terms of JPEG Image Quality and Gaussian Noise Levels
			19.5.3.1 Mapping the Impact for PSNR
			19.5.3.2 Mapping the Impact for ORB
			19.5.3.3 Mapping the Impact for SSIM
	19.6 Conclusions
	References
Chapter 20: Telemedicine: A New Opportunity for Transforming and Improving Rural India’s Healthcare
	20.1 Introduction
	20.2 Rural Healthcare
	20.3 Benefits of Telemedicine to Patients
	20.4 ISRO’S Move with Telemedicine
	20.5 Development Challenge
		20.5.1 Awareness Building
		20.5.2 Acceptance
		20.5.3 Availability
		20.5.4 Affordability
	20.6 Conclusion
	References