Object Detection by Stereo Vision Images
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.
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.
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Persons
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.
Content
Preface xiii
1 Data Conditioning for Medical Imaging 1
Shahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi
1.1 Introduction 2
1.2 Importance of Image Preprocessing 2
1.3 Introduction to Digital Medical Imaging 3
1.3.1 Types of Medical Images for Screening 4
1.3.1.1 X-rays 4
1.3.1.2 Computed Tomography (CT) Scan 4
1.3.1.3 Ultrasound 4
1.3.1.4 Magnetic Resonance Imaging (MRI) 5
1.3.1.5 Positron Emission Tomography (PET) Scan 5
1.3.1.6 Mammogram 5
1.3.1.7 Fluoroscopy 5
1.3.1.8 Infrared Thermography 6
1.4 Preprocessing Techniques of Medical Imaging Using Python 6
1.4.1 Medical Image Preprocessing 6
1.4.1.1 Reading the Image 7
1.4.1.2 Resizing the Image 7
1.4.1.3 Noise Removal 8
1.4.1.4 Filtering and Smoothing 9
1.4.1.5 Image Segmentation 11
1.5 Medical Image Processing Using Python 13
1.5.1 Medical Image Processing Methods 16
1.5.1.1 Image Formation 17
1.5.1.2 Image Enhancement 19
1.5.1.3 Image Analysis 19
1.5.1.4 Image Visualization 19
1.5.1.5 Image Management 19
1.6 Feature Extraction Using Python 20
1.7 Case Study on Throat Cancer 24
1.7.1 Introduction 24
1.7.1.1 HSI System 25
1.7.1.2 The Adaptive Deep Learning Method Proposed 25
1.7.2 Results and Findings 27
1.7.3 Discussion 28
1.7.4 Conclusion 29
1.8 Conclusion 29
References 30
Additional Reading 31
Key Terms and Definition 32
2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study 33
Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar
2.1 Introduction 33
2.2 Literature Review 35
2.3 Learning Methods 41
2.3.1 Machine Learning 41
2.3.2 Deep Learning 42
2.3.3 Transfer Learning 42
2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques 43
2.4.1 Dataset Description 43
2.4.2 Evaluation Platform 44
2.4.3 Training Process 44
2.4.4 Model Evaluation of CNN Classifier 46
2.4.5 Mathematical Model 47
2.4.6 Parameter Optimization 47
2.4.7 Performance Metrics 50
2.5 Conclusion 52
References 53
3 Contamination Monitoring System Using IOT and GIS 57
Kavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari
3.1 Introduction 58
3.2 Literature Survey 58
3.3 Proposed Work 60
3.4 Experimentation and Results 61
3.4.1 Experimental Setup 61
3.5 Results 64
3.6 Conclusion 70
Acknowledgement 71
References 71
4 Video Error Concealment Using Particle Swarm Optimization 73
Rajani P. K. and Arti Khaparde
4.1 Introduction 74
4.2 Proposed Research Work Overview 75
4.3 Error Detection 75
4.4 Frame Replacement Video Error Concealment Algorithm 77
4.5 Research Methodology 77
4.5.1 Particle Swarm Optimization 78
4.5.2 Spatio-Temporal Video Error Concealment Method 78
4.5.3 Proposed Modified Particle Swarm Optimization Algorithm 79
4.6 Results and Analysis 83
4.6.1 Single Frame With Block Error Analysis 85
4.6.2 Single Frame With Random Error Analysis 86
4.6.3 Multiple Frame Error Analysis 88
4.6.4 Sequential Frame Error Analysis 91
4.6.5 Subjective Video Quality Analysis for Color Videos 93
4.6.6 Scene Change of Videos 94
4.7 Conclusion 95
4.8 Future Scope 97
References 97
5 Enhanced Image Fusion with Guided Filters 99
Nalini Jagtap and Sudeep D. Thepade
5.1 Introduction 100
5.2 Related Works 100
5.3 Proposed Methodology 102
5.3.1 System Model 102
5.3.2 Steps of the Proposed Methodology 104
5.4 Experimental Results 104
5.4.1 Entropy 104
5.4.2 Peak Signal-to-Noise Ratio 105
5.4.3 Root Mean Square Error 107
5.4.3.1 Qab/f 108
5.5 Conclusion 108
References 109
6 Deepfake Detection Using LSTM-Based Neural Network 111
Tejaswini Yesugade, Shrikant Kokate, Sarjana Patil, Ritik Varma and Sejal Pawar
6.1 Introduction 111
6.2 Related Work 112
6.2.1 Deepfake Generation 112
6.2.2 LSTM and CNN 112
6.3 Existing System 113
6.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking 113
6.3.2 Detection Using Inconsistence in Head Pose 113
6.3.3 Exploiting Visual Artifacts 113
6.4 Proposed System 114
6.4.1 Dataset 114
6.4.2 Preprocessing 114
6.4.3 Model 115
6.5 Results 117
6.6 Limitations 119
6.7 Application 119
6.8 Conclusion 119
References 119
7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey 121
Kavita Shinde and Anuradha Thakare
7.1 Introduction 121
7.2 Related Work 123
7.3 Evaluation of Related Research 129
7.4 General Framework for Fetal Brain Abnormality Classification 129
7.4.1 Image Acquisition 130
7.4.2 Image Pre-Processing 130
7.4.2.1 Image Thresholding 130
7.4.2.2 Morphological Operations 131
7.4.2.3 Hole Filling and Mask Generation 131
7.4.2.4 MRI Segmentation for Fetal Brain Extraction 132
7.4.3 Feature Extraction 132
7.4.3.1 Gray-Level Co-Occurrence Matrix 133
7.4.3.2 Discrete Wavelet Transformation 133
7.4.3.3 Gabor Filters 134
7.4.3.4 Discrete Statistical Descriptive Features 134
7.4.4 Feature Reduction 134
7.4.4.1 Principal Component Analysis 135
7.4.4.2 Linear Discriminant Analysis 136
7.4.4.3 Non-Linear Dimensionality Reduction Techniques 137
7.4.5 Classification by Using Machine Learning Classifiers 137
7.4.5.1 Support Vector Machine 138
7.4.5.2 K-Nearest Neighbors 138
7.4.5.3 Random Forest 139
7.4.5.4 Linear Discriminant Analysis 139
7.4.5.5 Naïve Bayes 139
7.4.5.6 Decision Tree (DT) 140
7.4.5.7 Convolutional Neural Network 140
7.5 Performance Metrics for Research in Fetal Brain Analysis 141
7.6 Challenges 142
7.7 Conclusion and Future Works 142
References 143
8 Analysis of COVID-19 Data Using Machine Learning Algorithm 147
Chinnaiah Kotadi, Mithun Chakravarthi K., Srihari Chintha and Kapil Gupta
8.1 Introduction 147
8.2 Pre-Processing 148
8.3 Selecting Features 149
8.4 Analysis of COVID-19-Confirmed Cases in India 152
8.4.1 Analysis to Highest COVID-19-Confirmed Case States in India 153
8.4.2 Analysis to Highest COVID-19 Death Rate States in India 153
8.4.3 Analysis to Highest COVID-19 Cured Case States in India 154
8.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State 155
8.5 Linear Regression Used for Predicting Daily Wise COVID- 19
Cases in Maharashtra 156
8.6 Conclusion 157
References 157
9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering 159
Manish Sharma and Rutuja Deshmukh
9.1 Introduction 160
9.2 Related Work 162
9.3 Recommender Systems and Collaborative Filtering 164
9.4 Proposed Methodology 165
9.5 Experiment Analysis 167
9.6 Conclusion 168
References 168
10 Virtual Moratorium System 171
Manisha Bhende, Muzasarali Badger, Pranish Kumbhar, Vedanti Bhatkar and Payal Chavan
10.1 Introduction 172
10.1.1 Objectives 172
10.2 Literature Survey 172
10.2.1 Virtual Assistant-BLU 172
10.2.2 HDFC Ask EVA 173
10.3 Methodologies of Problem Solving 173
10.4 Modules 174
10.4.1 Chatbot 174
10.4.2 Android Application 175
10.4.3 Web Application 175
10.5 Detailed Flow of Proposed Work 176
10.5.1 System Architecture 176
10.5.2 DFD Level 1 177
10.6 Architecture Design 178
10.6.1 Main Server 178
10.6.2 Chatbot 178
10.6.3 Database Architecture 180
10.6.4 Web Scraper 180
10.7 Algorithms Used 181
10.7.1 AES-256 Algorithm 181
10.7.2 Rasa NLU 181
10.8 Results 182
10.9 Discussions 183
10.9.1 Applications 183
10.9.2 Future Work 183
10.9.3 Conclusion 183
References 183
11 Efficient Land Cover Classification for Urban Planning 185
Vandana Tulshidas Chavan and Sanjeev J. Wagh
11.1 Introduction 185
11.2 Literature Survey 189
11.3 Proposed Methodology 191
11.4 Conclusion 192
References 192
12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review 195
Pradnya Patil and Sanjeev J. Wagh
12.1 Introduction 196
12.2 Literature Survey 196
12.3 Problem Statement and Objectives 201
12.3.1 Problem Statement 201
12.3.2 Objectives 201
12.4 Proposed Methodology 202
12.4.1 Pre-Processing 202
12.4.2 Feature Extraction 203
12.4.3 Classification 203
12.5 Conclusion 204
References 204
13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering 207
Anupama Patil, Manisha Bhende, Suvarna Patil and P. P. Shevatekar
13.1 Introduction 208
13.2 Related Work 210
13.3 Distance Measurement Using Stereo Vision 213
13.3.1 Calibration of the Camera 215
13.3.2 Stereo Image Rectification 215
13.3.3 Disparity Estimation and Stereo Matching 216
13.3.4 Measurement of Distance 217
13.4 Object Segmentation in Depth Map 218
13.4.1 Formation of Depth Map 218
13.4.2 Density-Based in 3D Object Grouping Clustering 218
13.4.3 Layered Images Object Segmentation 219
13.4.3.1 Image Layer Formation 221
13.4.3.2 Determination of Object Boundaries 222
13.5 Conclusion 223
References 224
14 Real-Time Depth Estimation Using BLOB Detection/ Contour Detection 227
Arokia Priya Charles, Anupama V. Patil and Sunil Dambhare
14.1 Introduction 227
14.2 Estimation of Depth Using Blob Detection 229
14.2.1 Grayscale Conversion 230
14.2.2 Thresholding 231
14.2.3 Image Subtraction in Case of Input with Background 232
14.2.3.1 Preliminaries 233
14.2.3.2 Computing Time 234
14.3 Blob 234
14.3.1 BLOB Extraction 234
14.3.2 Blob Classification 235
14.3.2.1 Image Moments 236
14.3.2.2 Centroid Using Image Moments 238
14.3.2.3 Central Moments 238
14.4 Challenges 241
14.5 Experimental Results 241
14.6 Conclusion 251
References 255
Index 257
Preface
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. Therefore, 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.
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. Briefly stated, since it is a pioneer reference in this particular field, it will be a significant source of information for researchers who have been longing for an integrated reference. It is ideally designed for researchers, academics, and post-graduate students seeking current research on emerging soft computing areas; and it can also be used by various universities as a textbook for graduate/post-graduate courses. Many professional societies, IT professionals, or organizations working in the field of robotics will also benefit from this book.
A chapter-by-chapter synopsis of the topics covered in this book follows:
- In Chapter 1, Deepti Nikumbh et al. present data conditioning techniques for medical imaging. Digital images have a tremendous influence on today's world and have become an essential component in the clinical medical field. Significant advancements in the processing of medical images, and improvements in diagnosis and analysis, have transformed medical imaging into one of today's hottest emerging fields for implementation and research. Image pre-processing techniques along with image segmentation and image processing algorithms are useful tools that pave the way for advancement in the medical field with wide applications such as cancer detection, fingerprint identification and many others using pattern matching, feature extraction and edge detection algorithms.
- In Chapter 2, Shravani Nimbolkar et al. present an analytical study for pneumonia detection using machine learning and deep learning techniques. This chapter studies different types of lung diseases and how their diagnosis can be aided using these techniques. Experimentation with different machine learning models, like CNN and MLP, and pre-trained architectures, like VGG16 and ResNet, are used to predict pneumonia from chest X-rays.
- In Chapter 3, Kavita R. Singh et al. explore the advanced application of a contamination monitoring system using IoT and GIS. Contamination/pollution is one of the biggest challenges where environmental issues are concerned. The authors analyze particular areas that are more contaminated/polluted in Nagpur City, Maharashtra, India, by calibrating the air quality index as an IoT-based air pollution monitoring framework and plotting the data using a geographical information system. Additionally, the data analysis, which is done with the help of Tableau software and different parameters like air quality index, temperature, etc., is provided to the end user through the android application.
- In Chapter 4, Rajani P.K. et al. present the new area of video error concealment using particle swarm optimization. Video transmission over wired or wireless channels, such as the internet, is the fastest growing area of research. The proposed method is a novel method in the spatio-temporal domain that can significantly improve the subjective and objective video quality. There are many algorithms for video error concealment. These optimized algorithms should be used for obtaining better video quality. Particle swarm optimization (PSO), which is one of the best optimized bio-in-spired algorithms, is used to conceal the errors in different video formats. Correlation is used for detection of errors in the videos and each error frame is concealed using PSO algorithm in MATLAB.
- In Chapter 5, Nalini Jagtap explores enhanced image fusion with guided filters. She proposes the modified guided filtering approach called "novel guided filtering" to overcome blurring and ringing effects. The primary step in this approach is to design the guidance image and generate the base and complex components based on that image. The edge detection operator plays a significant role in deciding the guidance image. The focus map is generated using low-rank representation, which is based on a detailed part of the original image. The built-in characteristic of removing ringing and blurring effects using LRR helps to develop artifact-free/noiseless detail-enhanced image fusion. First, guided filters are applied on a focus map; then the guided filter output is used to generate the resultant all-in-one fused image. In this case, ringing and blurring effects are removed using guided filters in the resultant fused image.
- In Chapter 6, Tejaswini Yesugade proposes deepfake detection using LSTM-based neural network. The rapid growth of social media and new developments in deep generative networks have improved the quality of creating more realistic fake videos, which are called deepfake videos. Such deepfake videos are used in politics to create political turmoil, for blackmail, and terrorism. To reduce the harm that can be done using such methods and prevent the spread of such fake images or videos, the author proposes a method that can detect such deepfakes and a new method to detect AI-generated fake videos using an algorithm such as CNN and LSTM. This method will detect deepfakes by using ResNext50 and LSTM algorithms, which have an accuracy of around 88%.
- In Chapter 7, Kavita Shinde et al. present various approaches for classification of fetal brain abnormalities with MRI images. Magnetic resonance imaging of fetuses allows doctors to observe brain abnormalities early on. Therefore, since nearly three out of every 1,000 fetuses have a brain anomaly, it is necessary to determine and categorize them at an earlier stage. The literature survey finds less work is involved in the classification of abnormal fetal brain based on conventional methods of machine learning, while more related work is conducted for the segmentation and feature extraction using different techniques. In this chapter, the authors review different machine learning techniques used for the complete MRI processing chain, starting with image acquisition to its classification.
- In Chapter 8, Chinnaiah Kotadi et al. explore a method to analyze COVID-19 data using a machine learning algorithm. The authors analyze past COVID-19 data to raise awareness of COVID-19 second wave conditions and precautions against the delta variant. They also provide COVID-19 cases such as confirmed cases, cured patients' cases, and death rates in India. Also, by using a machine learning algorithm, the states of India in which the most cases and deaths occurred are provided.
- In Chapter 9, Manish Sharma et al. explore an intelligent recommendation system for evaluating teaching faculty performance using adaptive collaborative filtering. This system uses the deep learning model for the evaluation and enhancement of the performance of teachers in educational institutions. To give a recommendation framework, this work incorporates numerous elements such as student assessment, intake quality, innovative practices, experiential learning approaches, and so on. The dataset derived from an educational institute's ERP was used to train and test the proposed recommender. The performance of the proposed recommender system was evaluated using the real-time data of teachers and other stakeholders from an educational institute apart from some secondary parameters. The comparative analysis of various techniques along with the performance comparison based on accuracy, precision, and recall are well furnished.
- In Chapter 10, Manisha Blende et al. propose a virtual moratorium system. By using the proposed system, the banker will get all the information regarding a customer who has opted for the moratorium. The user will interact with the chatbot and submit the moratorium request and then chatbot will ask questions based on customers' responses. Rasa natural language processing (NLP) and Rasa natural language understanding algorithm will classify the intents from the user responses. Intents will be compared with predefined patterns to extract the specific data. These responses will be stored in the NoSQL (MongoDB) database, and these data will be shared with the banker, who will further analyze them. The main purpose of this study is to help the banker know whether the customer who has applied for the moratorium is genuine. With this system, both the customer and the banker will be able to save time and effort. The proposed system will allow...
