Advanced Sensing in Image Processing and IoT

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Chapter 1 Machine Learning–Based Early Fire Detection System Using a Low-Cost Drone
	1.1 Introduction
		1.1.1 Motivation
	1.2 Materials and Methods
		1.2.1 Software Design
	1.3 Results
	1.4 Conclusions
	Acknowledgments
	Conflicts of Interest
	References
Chapter 2 Computer Vision: Practical Approach to Facial Detection Techniques for Security Applications
	2.1 Introduction
	2.2 Facial Detection
	2.3 Facial Detection Techniques
		2.3.1 Haar Cascade Classifier
		2.3.2 MMOD Face Detector
		2.3.3 HOG Face Detector
		2.3.4 MTCNN Face Detector
		2.3.5 DNN Face Detector
	2.4 Results and comparative analysis
		2.4.1 Count of Detected Faces and Their Time Analysis
		2.4.2 Confusion Matrix
		2.4.3 Other Evaluation Parameters
		2.4.4 Comparative Analysis
	2.5 Conclusion
	References
Chapter 3 Image Segmentation: Classification and Implementation Techniques
	3.1 Introduction
	3.2 How Image Segmentation Works
	3.3 Applications of Digital Image Processing
		3.3.1 Image Sharpening and Restoration
		3.3.2 Medical Field
			3.3.2.1 Ultraviolet Imaging
			3.3.2.2 Transmission and Encoding
			3.3.2.3 Machine/Robot Vision
			3.3.2.4 Obstacle Detection
			3.3.2.5 Line Follower Robot
			3.3.2.6 Color Processing
			3.3.2.7 Pattern Recognition
			3.3.2.8 Video Processing
	3.4 Requirement for Image Segmentation
		3.4.1 Face Recognition
		3.4.2 Number Plate Identification
		3.4.3 Image-Based Search
		3.4.4 Medical Imaging
	3.5 Types of Image Segmentation
		3.5.1 Approach-Based Classification
			3.5.1.1 Region-Based Approach (Similarity Detection)
			3.5.1.2 Boundary-Based Approach (Discontinuity Detection)
		3.5.2 Technique-Based Classification
			3.5.2.1 Structural Techniques
			3.5.2.2 Stochastic Techniques
			3.5.2.3 Combined/Hybrid Techniques
	3.6 Image Segmentation Techniques
		3.6.1 Thresholding Segmentation
			3.6.1.1 Simple Thresholding
			3.6.1.2 Otsu’s Binarization
			3.6.1.3 Adaptive Thresholding
		3.6.2 Edge-Based Segmentation
			3.6.2.1 Search-Based Edge Detection
			3.6.2.2 Zero Crossing–Based Edge Detection
		3.6.3 Region-Based Segmentation
			3.6.3.1 Region Growing
			3.6.3.2 Region Splitting and Merging
		3.6.4 Watershed Segmentation
		3.6.5 Clustering-Based Segmentation Algorithms
			3.6.5.1 K-Means Clustering
			3.6.5.2 Fuzzy C Means
		3.6.6 Neural Networks for Segmentation
	3.7 Implementation and Pre-Requisites
	3.8 Future Scope
	3.9 Conclusion
	References
Chapter 4 Image Processing with IoT for Patient Monitoring
	4.1 Introduction
	4.2 IoT in the Medical Domain
		4.2.1 Data Communication between Different Layers in IoT
			4.2.1.1 Internet of Healthcare Things (IoHT) Network Layer
			4.2.1.2 Fog Computing Layer
			4.2.1.3 Communication Interface
			4.2.1.4 Cloud Layer
	4.3 Application Areas of Medical IoT
		4.3.1 Patient Monitoring and Tracking
		4.3.2 IoT for Big Data
		4.3.3 IoT Wearable Devices
		4.3.4 Emergency Services
		4.3.5 Smart Computing
		4.3.6 Smart Nodes
	4.4 Image Processing in Medical IoT
		4.4.1 Remote Patient Monitoring
		4.4.2 Preventive Care and Monitoring
		4.4.3 Clinical Monitoring
		4.4.4 Medical Service Organization
		4.4.5 Different Applications Equipped with Image Processing
			4.4.5.1 Proposed System
			4.4.5.2 System Description
			4.4.5.3 Communication System
			4.4.5.4 Disease Recognition
			4.4.5.5 Image Acquisition and Pre-Processing
			4.4.5.6 Image Segmentation
			4.4.5.7 Feature Extraction
			4.4.5.8 Advantages of Proposed Application
			4.4.5.9 Challenges of Application
	4.5 Benefits and Limitations of IoT
	4.6 Future Scope
	4.7 Conclusion
	References
Chapter 5 Theory, Practical Concepts, Strategies and Methods for Emotion Recognition
	5.1 Introduction
		5.1.1 Human Behavior and Emotions
	5.2 Emotion Recognition and Its Types
		5.2.1 Types of Emotion Recognition
		5.2.2 Literature Review
	5.3 Technologies Used In Emotion Recognition:
		5.3.1 Image Processing
			5.3.1.1 Benefits of Image Processing
		5.3.2 OpenCV
		5.3.3 Python
		5.3.4 Deep Learning and Convolutional Neural Networks
	5.4 Methodology
		5.4.1 Hands on Approach of Emotion Recognition with CNN
			5.4.1.1 Data Source
			5.4.1.2 Preprocessing
			5.4.1.3 Convolutional Neural Network (CNN) Setup
			5.4.1.4 Model Training
		5.4.2 Emotion Recognition Using DeepFace Framework
			5.4.2.1 Hands on for Installation of DeepFace
			5.4.2.2 Functions Used in DeepFace
			4.4.2.3 Current Uses
	5.5 Applications
		5.5.1 Drawbacks
	5.6 Test Results
		5.6.1 Emotion Recognition Using DeepFace Result
		5.6.2 Emotion Recognition Using Convolutional Neural Network
	Bibliography
Chapter 6 A Comparative Study of Convolutional Neural Networks for Plant Phenology Recognition
	6.1 Introduction
	6.2 Related Works
	6.3 Background
		6.3.1 Deep Learning
			6.3.1.1 Deep Learning Usage in Crop Production
			6.3.1.2 Various Methods in Plant Subject Area
		6.3.2 Convolutional Neural Networks
			6.3.2.1 2-D CNNs
			6.3.2.2 3-DCNNs
			6.3.2.3 Methods of Regularization
	6.4 CNN Performance
		6.4.1 Comparing CNN with Other Methods
		6.4.2 Generalized Productivity
	6.5 Materials and Methods
		6.5.1 Convolutional Neural Network Models
		6.5.2 Datasets of Training and Testing
	6.6 Results and Discussion
	6.7 Conclusion
	References
Chapter 7 IoT and Wearable Sensors for Health Monitoring
	7.1 Introduction
	7.2 Covid-19: Importance of Wearable Sensing Technology
	7.3 Sensors and Types of Sensors
		7.3.1 Types of Sensors Used in Wearable Technology
			7.3.1.1 Accelerometer
			7.3.1.2 Gyroscopes
			7.3.1.3 Magnetometers
			7.3.1.4 Global Positioning System (GPS)
			7.3.1.5 Heart Rate Sensors
			7.3.1.6 Pedometers
			7.3.1.7 Pressure Sensors
			7.3.1.8 Integration of Sensors into Wearables (Microcontroller)
	7.4 Internet of Things
		7.4.1 Network of the IoT
		7.4.2 IoT-Based Wearable Healthcare System
	7.5 Future Perspective
	7.6 Conclusion
	References
Chapter 8 Analysis of Interpolation-Based Image In-Painting Approaches
	8.1 Introduction
	8.2 Literature Review and Background
		8.2.1 Cubic Interpolation
		8.2.2 Kriging Interpolation
		8.2.3 Radial Basis Functions
		8.2.4 High-Dimensional Model Representation and Lagrange Interpolation
	8.3 Materials and Methods
		8.3.1 Materials
		8.3.2 Method
			8.3.2.1 Two-Dimensional Cubic Interpolation
			8.3.2.2 Kriging Interpolation
			8.3.2.3 Interpolation with Radial-Based Functions
			8.3.2.4 Interpolation Using High-Dimensional Model Representation
	8.4 Results
	8.5 Conclusion
	References
Chapter 9 Real Time Density–Based Traffic Congestion Detection System Using Image Processing and Fuzzy Logic Controller
	9.1 Introduction
	9.2 Related Work
	9.3 Proposed System Model
		9.3.1 Moving Vehicle Detection and Counting System
		9.3.2 Parameter Extraction Using SUMO Simulator
		9.3.3 Key Features Extraction using Fuzzy C-Means Clustering
		9.3.4 Traffic Congestion Level Estimation Using Fuzzy Logic Controller
	9.4 Experimental Analysis and Results
	9.5 Conclusion
	References
	Annexure 9.1
	Annexure 9.2
	Annexure 9.3
		Algorithm: Fuzzy C-means clustering [28,29]
Chapter 10 Fundamentals of Face Recognition with IoT
	10.1 Introduction
	10.2 Process of Face Recognition
		10.2.1 Fundamentals of Face Recognition Steps
			10.2.1.1 Face Detection
			10.2.1.2 Pre-Processing Image
			10.2.1.3 Feature Extraction
			10.2.1.4 Optimal Feature Selection and Reduction
			10.2.1.5 Classification
	10.3 System Architecture of IoT and Face Application
	10.4 Table of Comparison
	10.5 Challenges and Limitations
	10.6 Conclusions
	References
Chapter 11 IoT for Health Monitoring
	11.1 Introduction
	11.2 Literature Review
	11.3 Proposed Methodology
	11.4 Hardware and Software Specification
		11.4.1 Arduino Uno
		11.4.2 Temperature Sensor
		11.4.3 LCD
		11.4.4 ESP8266
		11.4.5 Power Supply
		11.4.6 Pulse Sensors
	11.5 Software Specification
		11.5.1 Arduino IDE
		11.5.2 ThingSpeak (API)
	11.6 Results and Discussion
		11.6.1 Phases 1 and 2: Patient’sVitals Are CollectedandPushed to the Cloud, Where They Are Graphically Analysed
		11.6.2 MATLAB Analysis of 3-Day Body Temperature of Patients
		11.6.3 ThingSpeak Dashboard with All the Vital Parameters and Their Graphical Representation
		11.6.4 Phase 3: IFTTIntegration of Data from ThingSpeak to Generate Triggers at Particular Threshold Value
	11.7 Conclusion and Future Work
	References
Chapter 12 Human Behavior Detection using Image Processing and IoT
	12.1 Introduction
		12.1.1 What Is Computer Vision?
		12.1.2 Background of the Research
		12.1.3 Objective of the Project
		12.1.4 Scope of the Project
		12.1.5 Overview of Proposed System
		12.1.6 Project Organization
	12.2 Literature Review
		12.2.1 Local Shape-Based Human Detection
		12.2.2 Global Approach
		12.2.3 Local Approach: Implicit Shape Model
		12.2.4 Dense Descriptors of Image Regions
		12.2.5 Work in Human Detection
		12.2.6 Different Types of Edge Detector
			12.2.6.1 Sobel Operator
			12.2.6.2 Roberts Cross Operator
			12.2.6.3 Prewitt’s Operator
			12.2.6.4 Laplacian of Gaussian
		12.2.7 Canny Edge Detection Algorithm
		12.2.8 Detection and Tracking Using Combination of Thermal and Visible Imaging
			12.2.8.1 Segmentation
			12.2.8.2 Classification
			12.2.8.3 Summary
	12.3 Proposed Human Detection Methodology
		12.3.1 Introduction
		12.3.2 Proposed System Architecture
		12.3.3 Details of Human Detection
			12.3.3.1 Human Detection
			12.3.3.2 Image Acquisition
			12.3.3.3 Gray Scale Conversion
			12.3.3.4 Edge Detection
			12.3.3.5 Summary
	12.4 Experiments, Results, and Discussion
		12.4.1 Introduction
		12.4.2 Experiment Setup
		12.4.3 Experimental Results of Proposed System
	12.5 Conclusion and Future Work
		12.5.1 Contribution
		12.5.2 Limitations and Future Work
		12.5.3 Concluding Remarks
	References
Chapter 13 A Novel Cross-Slotted Dual-Band Fractal Microstrip Antenna Design for Internet of Things (IoT) Applications
	13.1 Introduction
	13.2 Related Work
	13.3 Fractal Antenna Design and Measurements
		13.3.1 Different Stages of Antenna Creation
		13.3.2 Parameters for Antenna Characterization
	13.4 Simulation Results of Cross-slotted Antenna
	13.5 Measurements of Fabricated Cross-Slotted Fractal Antenna
		13.5.1 Return Loss and Voltage Standing Wave Ratio
	13.6 Conclusion
	References
Chapter 14 Examination of Vegetation Health and Its Relation with Normalized Difference Built-Up Index: A Study on Rajarhat Block of North 24 Parganas District of West Bengal, India
	14.1 Introduction
	14.2 Materials and Methods
		14.2.1 Normalised Difference Vegetation Index (NDVI)
		14.2.2 Normalized Difference Built-Up Index (NDBI)
	14.3 Results and Discussion
		14.3.1 NDVI and NDBI Scenario of 1999
		14.3.2 NDVI and NDBI Scenario of 2009
		14.3.3 NDVI and NDBI Scenario of 2019
		14.3.4 Temporal Change of Land Use Classified on the Basis of NDVI Values
		14.3.5 Temporal Analysis of NDVI and NDBI
		14.3.6 Analysing the Relationship between the NDVI and NDBI of the Study Area
	14.4 Conclusion
	Acknowledgement
	References
Chapter 15 Image Processing Implementation for Medical Images to Detect and Classify Various Diseases on the Basis of MRI and Ultrasound Images
	15.1 Introduction to Medical Images
		15.1.1 Computed Tomography (CT)
		15.1.2 Ultrasound
		15.1.3 Magnetic Resonance Imaging (MRI)
		15.1.4 Fluoroscopy
		15.1.5 Ophthalmic Imaging
	15.2 Human Body Diseases Detected by Image Processing Techniques
		15.2.1 Kidney Stone
		15.2.2 Breast Cancer
		15.2.3 Brain Tumor
	15.3 Image Processing Techniques to Detect Abnormalities
		15.3.1 Image Acquisition
		15.3.2 Image Preprocessing (Conversion RGB to Gray)
		15.3.3 Image Contrast Enhancement by Intensity Adjustment
		15.3.4 Median Filter
		15.3.5 Segmentation
			15.3.5.1 Clustering Segmentation
			15.3.5.2 Threshold Segmentation
			15.3.5.3 Morphological Operation for Area Localization
	15.4 Classification by Convolution Neural Networks
	15.5 Result Analysis
	15.6 Conclusion
	References
Chapter 16 Benchmarking of Medical Imaging Technologies
	16.1 Introduction
	16.2 Imaging Techniques
		16.2.1 Traditional Film Radiography
		16.2.2 Imaging Radiography
		16.2.3 Computed Tomography
		16.2.4 Magnetic Resonance Imaging (MRI)
		16.2.5 Ultrasonography
		16.2.6 Atomic Medicine
		16.2.7 Scintigraphy
		16.2.8 Positron Emission Tomography (PET)
	16.3 Other Imaging Techniques
		16.3.1 Electrical Impedance Tomography (EIT)
		16.3.2 Optical Coherence Tomography (OCT)
		16.3.3 Photoacoustic/Thermoacoustic Imaging
		16.3.4 Microwave Imaging
		16.3.5 Magnetic Resonance Elastography (MRE)
	16.4 Requirement for Several Imaging Modalities
	16.5 Picture Quality, Image Processing, and Visualization of Images
	16.6 Parts of Image Processing System
		16.6.1 Picture Processing
		16.6.2 Picture Improvement
		16.6.3 Shading Handling
		16.6.4 Wavelets
		16.6.5 Division
		16.6.6 Portrayal
		16.6.7 Description
		16.6.8 Acknowledge
	16.7 Radiation Exposure and Radiation Protection in Medical Imaging
	16.8 General Applications of Medical Imaging: Imaging towards Diseases
		16.8.1 Alzheimer’s Disease (AD)
		16.8.2 Malignant Growth
		16.8.3 Cardiovascular Diseases
		16.8.4 Neonatal Abstinence Disorder (NAD)
		16.8.5 Imaging in Drug Development
		16.8.6 Imaging in Medical Device Manufacturing
	16.9 Conclusion
		16.9.1 Future Aspects of Medical Imaging
	References
Chapter 17 Application of Image Processing in Plant Leaf Disease Detection
	17.1 Introduction
	17.2 Contributions in the Field of Leaf Disease Detection
	17.3 Leaf Disease Detection Using Convolutional Neural Networks
	17.4 Results and Observations
	17.5 Conclusion
	References
Chapter 18 Monitoring Air Pollution with the Help of Tree Bark and Advanced Technology IoT and AI Techniques at Indore City
	18.1 Introduction
	18.2 Literature Survey
	18.3 Aim and Objective
	18.4 Study Area
	18.5 Pollution Areas
	18.6 Experimental Trees
	18.7 Material
	18.8 Methods
	18.9 Observation
	18.10 Results and Discussion
	18.11 Challenges and Possibilities
	18.12 Conclusion
	Acknowledgments
	References
Chapter 19 IoT-Based Smart Stick for the Blind: A Review
	19.1 Introduction
	19.2 System Model
		19.2.1 Environment Sensing and Obstacle Detection
			19.2.1.1 Some Commonly Used Sensors
			19.2.1.2 Some of the Most Commonly Used Microcontroller Boards
		19.2.2 Communication Messages and Alerts
		19.2.3 Tracking
		19.2.4 Other Enhanced Features
	19.3 Issues and Challenges
	19.4 Conclusion and Future Work
	References
Index