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Object Detection by Stereo Vision Images

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OBJECT DETECTION BY STEREO VISION IMAGES

Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers.

Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems.

Audience

Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.

Author(s): R. Arokia Priya, Anupama V. Patil, Manisha Bhende, Sanjeev Wagh, Anuradha D. Thakare
Publisher: Wiley-Scrivener
Year: 2022

Language: English
Pages: 282
City: Beverly

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