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Object Detection by Stereo Vision Images

Edited by R. Arokia Priya, Anupama V Patil, Manisha Bhende, Anuradha Thakare and Sanjeev Wagh
Copyright: 2022   |   Status: Published
ISBN: 9781119842194  |  Hardcover  |  
280 pages | 112 illustrations
Price: $195 USD
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One Line Description
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.

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.

Description
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.

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Author / Editor Details
R Arokia Priya, PhD, is Head of Electronics & Telecommunication Department at Dr. D Y Patil Institute of Engineering, Management and Research, Pune, India. She has 20 years of experience in this field as well as more than 40 publications, one patent and two copyrights to her credit.

Anupama V Patil, PhD, is the Principal at Dr. D Y Patil Institute of Engineering, Management and Research, Pune, India. She has more than 30 years of experience in this field as well as more than 40 publications and 1 patent to her credit.

Manisha Bhende, PhD, is a professor at the Marathwada Mitra Mandals Institute of Technology, Pune, India. She has 23 years of experience in this field as well as 39 research papers in international and national conferences and journals, and has published five patents and four copyrights to her credit.

Anuradha Thakare, PhD, is a professor in the Department of Computer Engineering at Pimpri Chinchwad College of Engineering, Pune, India. She has 20 years of experience in academics and research, with 78 research publications and eight IPR’s (Patents and Copyrights) to her credit.

Sanjeev Wagh, PhD, is a Professor in the Department of Information Technology at Govt. College of Engineering, Karad, India. He has 71 research papers to his credit.

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Table of Contents
Preface
1. Data Conditioning for Medical Imaging
Shahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi
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
2. Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study
Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar
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
3. Contamination Monitoring System Using IOT and GIS
Kavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari
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
4. Video Error Concealment Using Particle Swarm Optimization
Rajani P. K. and Arti Khaparde
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
5. Enhanced Image Fusion with Guided Filters
Nalini Jagtap and Sudeep D. Thepade
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
6. Deepfake Detection Using LSTM-Based Neural Network
Tejaswini Yesugade, Shrikant Kokate, Sarjana Patil, Ritik Varma and Sejal Pawar
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
7. Classification of Fetal Brain Abnormalities with MRI Images: A Survey
Kavita Shinde and Anuradha Thakare
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
8. Analysis of COVID-19 Data Using Machine Learning Algorithm
Chinnaiah Kotadi, Mithun Chakravarthi K., Srihari Chintha and Kapil Gupta
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
9. Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering
Manish Sharma and Rutuja Deshmukh
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
10. Virtual Moratorium System
Manisha Bhende, Muzasarali Badger, Pranish Kumbhar, Vedanti Bhatkar and Payal Chavan
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
11. Efficient Land Cover Classification for Urban Planning
Vandana Tulshidas Chavan and Sanjeev J. Wagh
11.1 Introduction
11.2 Literature Survey
11.3 Proposed Methodology
11.4 Conclusion
References
12. Data-Driven Approches for Fake News Detection on Social Media Platforms: Review
Pradnya Patil and Sanjeev J. Wagh
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
13. Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering
Anupama Patil, Manisha Bhende, Suvarna Patil and P. P. Shevatekar
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
14. Real-Time Depth Estimation Using BLOB Detection/Contour Detection
Arokia Priya Charles, Anupama V. Patil and Sunil Dambhare
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

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