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