Object Detection by Stereo Vision Images

Cover
	Title Page
	Copyright Page
Preface
	Contents
Chapter 1 Data Conditioning for Medical Imaging
	1.1 Introduction
	1.2 Importance of Image Preprocessing
	1.3 Introduction to Digital Medical Imaging
		1.3.1 Types of Medical Images for Screening
			1.3.1.1 X-rays
			1.3.1.2 Computed Tomography (CT) Scan
			1.3.1.3 Ultrasound
			1.3.1.4 Magnetic Resonance Imaging (MRI)
			1.3.1.5 Positron Emission Tomography (PET) Scan
			1.3.1.6 Mammogram
			1.3.1.7 Fluoroscopy
			1.3.1.8 Infrared Thermography
	1.4 Preprocessing Techniques of Medical Imaging Using Python
		1.4.1 Medical Image Preprocessing
			1.4.1.1 Reading the Image
			1.4.1.2 Resizing the Image
			1.4.1.3 Noise Removal
			1.4.1.4 Filtering and Smoothing
			1.4.1.5 Image Segmentation
	1.5 Medical Image Processing Using Python
		1.5.1 Medical Image Processing Methods
			1.5.1.1 Image Formation
			1.5.1.2 Image Enhancement
			1.5.1.3 Image Analysis
			1.5.1.4 Image Visualization
			1.5.1.5 Image Management
	1.6 Feature Extraction Using Python
	1.7 Case Study on Throat Cancer
		1.7.1 Introduction
			1.7.1.1 HSI System
			1.7.1.2 The Adaptive Deep Learning Method Proposed
		1.7.2 Results and Findings
		1.7.3 Discussion
		1.7.4 Conclusion
	1.8 Conclusion
	References
	Additional Reading
	Key Terms and Definition
Chapter 2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study
	2.1 Introduction
	2.2 Literature Review
	2.3 Learning Methods
		2.3.1 Machine Learning
		2.3.2 Deep Learning
		2.3.3 Transfer Learning
	2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques
		2.4.1 Dataset Description
		2.4.2 Evaluation Platform
		2.4.3 Training Process
		2.4.4 Model Evaluation of CNN Classifier
		2.4.5 Mathematical Model
		2.4.6 Parameter Optimization
		2.4.7 Performance Metrics
	2.5 Conclusion
	References
Chapter 3 Contamination Monitoring System Using IOT and GIS
	3.1 Introduction
	3.2 Literature Survey
	3.3 Proposed Work
	3.4 Experimentation and Results
		3.4.1 Experimental Setup
	3.5 Results
	3.6 Conclusion
	Acknowledgement
	References
Chapter 4 Video Error Concealment Using Particle Swarm Optimization
	4.1 Introduction
	4.2 Proposed Research Work Overview
	4.3 Error Detection
	4.4 Frame Replacement Video Error Concealment Algorithm
	4.5 Research Methodology
		4.5.1 Particle Swarm Optimization
		4.5.2 Spatio-Temporal Video Error Concealment Method
		4.5.3 Proposed Modified Particle Swarm Optimization Algorithm
	4.6 Results and Analysis
		4.6.1 Single Frame With Block Error Analysis
		4.6.2 Single Frame With Random Error Analysis
		4.6.3 Multiple Frame Error Analysis
		4.6.4 Sequential Frame Error Analysis
		4.6.5 Subjective Video Quality Analysis for Color Videos
		4.6.6 Scene Change of Videos
	4.7 Conclusion
	4.8 Future Scope
	References
Chapter 5 Enhanced Image Fusion with Guided Filters
	5.1 Introduction
	5.2 Related Works
	5.3 Proposed Methodology
		5.3.1 System Model
		5.3.2 Steps of the Proposed Methodology
	5.4 Experimental Results
		5.4.1 Entropy
		5.4.2 Peak Signal-to-Noise Ratio
		5.4.3 Root Mean Square Error
			5.4.3.1 QAB/F
	5.5 Conclusion
	References
Chapter 6 Deepfake Detection Using LSTM-Based Neural Network
	6.1 Introduction
	6.2 Related Work
		6.2.1 Deepfake Generation
		6.2.2 LSTM and CNN
	6.3 Existing System
		6.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking
		6.3.2 Detection Using Inconsistence in Head Pose
		6.3.3 Exploiting Visual Artifacts
	6.4 Proposed System
		6.4.1 Dataset
		6.4.2 Preprocessing
		6.4.3 Model
	6.5 Results
	6.6 Limitations
	6.7 Application
	6.8 Conclusion
	References
Chapter 7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey
	7.1 Introduction
	7.2 Related Work
	7.3 Evaluation of Related Research
	7.4 General Framework for Fetal Brain Abnormality Classification
		7.4.1 Image Acquisition
		7.4.2 Image Pre-Processing
			7.4.2.1 Image Thresholding
			7.4.2.2 Morphological Operations
			7.4.2.3 Hole Filling and Mask Generation
			7.4.2.4 MRI Segmentation for Fetal Brain Extraction
		7.4.3 Feature Extraction
			7.4.3.1 Gray-Level Co-Occurrence Matrix
			7.4.3.2 Discrete Wavelet Transformation
			7.4.3.3 Gabor Filters
			7.4.3.4 Discrete Statistical Descriptive Features
		7.4.4 Feature Reduction
			7.4.4.1 Principal Component Analysis
			7.4.4.2 Linear Discriminant Analysis
			7.4.4.3 Non-Linear Dimensionality Reduction Techniques
		7.4.5 Classification by Using Machine Learning Classifiers
			7.4.5.1 Support Vector Machine
			7.4.5.2 K-Nearest Neighbors
			7.4.5.3 Random Forest
			7.4.5.4 Linear Discriminant Analysis
			7.4.5.5 Naïve Bayes
			7.4.5.6 Decision Tree (DT)
			7.4.5.7 Convolutional Neural Network
	7.5 Performance Metrics for Research in Fetal Brain Analysis
	7.6 Challenges
	7.7 Conclusion and Future Works
	References
Chapter 8 Analysis of COVID-19 Data Using Machine Learning Algorithm
	8.1 Introduction
	8.2 Pre-Processing
	8.3 Selecting Features
	8.4 Analysis of COVID-19–Confirmed Cases in India
		8.4.1 Analysis to Highest COVID-19–Confirmed Case States in India
		8.4.2 Analysis to Highest COVID-19 Death Rate States in India
		8.4.3 Analysis to Highest COVID-19 Cured Case States in India
		8.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State
	8.5 Linear Regression Used for Predicting Daily Wise COVID-19 Cases in Maharashtra
	8.6 Conclusion
	References
Chapter 9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering
	9.1 Introduction
	9.2 Related Work
	9.3 Recommender Systems and Collaborative Filtering
	9.4 Proposed Methodology
	9.5 Experiment Analysis
	9.6 Conclusion
	References
Chapter 10 Virtual Moratorium System
	10.1 Introduction
		10.1.1 Objectives
	10.2 Literature Survey
		10.2.1 Virtual Assistant—BLU
		10.2.2 HDFC Ask EVA
	10.3 Methodologies of Problem Solving
	10.4 Modules
		10.4.1 Chatbot
		10.4.2 Android Application
		10.4.3 Web Application
	10.5 Detailed Flow of Proposed Work
		10.5.1 System Architecture
		10.5.2 DFD Level 1
	10.6 Architecture Design
		10.6.1 Main Server
		10.6.2 Chatbot
		10.6.3 Database Architecture
		10.6.4 Web Scraper
	10.7 Algorithms Used
		10.7.1 AES-256 Algorithm
		10.7.2 Rasa NLU
	10.8 Results
	10.9 Discussions
		10.9.1 Applications
		10.9.2 Future Work
		10.9.3 Conclusion
	References
Chapter 11 Efficient Land Cover Classification for Urban Planning
	11.1 Introduction
	11.2 Literature Survey
	11.3 Proposed Methodology
	11.4 Conclusion
	References
Chapter 12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review
	12.1 Introduction
	12.2 Literature Survey
	12.3 Problem Statement and Objectives
		12.3.1 Problem Statement
		12.3.2 Objectives
	12.4 Proposed Methodology
		12.4.1 Pre-Processing
		12.4.2 Feature Extraction
		12.4.3 Classification
	12.5 Conclusion
	References
Chapter 13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering
	13.1 Introduction
	13.2 Related Work
	13.3 Distance Measurement Using Stereo Vision
		13.3.1 Calibration of the Camera
		13.3.2 Stereo Image Rectification
		13.3.3 Disparity Estimation and Stereo Matching
		13.3.4 Measurement of Distance
	13.4 Object Segmentation in Depth Map
		13.4.1 Formation of Depth Map
		13.4.2 Density-Based in 3D Object Grouping Clustering
		13.4.3 Layered Images Object Segmentation
			13.4.3.1 Image Layer Formation
			13.4.3.2 Determination of Object Boundaries
	13.5 Conclusion
	References
Chapter 14 Real-Time Depth Estimation Using BLOB Detection/Contour Detection
	14.1 Introduction
	14.2 Estimation of Depth Using Blob Detection
		14.2.1 Grayscale Conversion
		14.2.2 Thresholding
		14.2.3 Image Subtraction in Case of Input with Background
			14.2.3.1 Preliminaries
			14.2.3.2 Computing Time
	14.3 BLOB
		14.3.1 BLOB Extraction
		14.3.2 Blob Classification
			14.3.2.1 Image Moments
			14.3.2.2 Centroid Using Image Moments
			14.3.2.3 Central Moments
	14.4 Challenges
	14.5 Experimental Results
	14.6 Conclusion
	References
Index
	EULA