Object Detection by Stereo Vision Images

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.

• Preface xiii1 Data Conditioning for Medical Imaging 1Shahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi1.1 Introduction 21.2 Importance of Image Preprocessing 21.3 Introduction to Digital Medical Imaging 31.3.1 Types of Medical Images for Screening 41.3.1.1 X-rays 41.3.1.2 Computed Tomography (CT) Scan 41.3.1.3 Ultrasound 41.3.1.4 Magnetic Resonance Imaging (MRI) 51.3.1.5 Positron Emission Tomography (PET) Scan 51.3.1.6 Mammogram 51.3.1.7 Fluoroscopy 51.3.1.8 Infrared Thermography 61.4 Preprocessing Techniques of Medical Imaging Using Python 61.4.1 Medical Image Preprocessing 61.4.1.1 Reading the Image 71.4.1.2 Resizing the Image 71.4.1.3 Noise Removal 81.4.1.4 Filtering and Smoothing 91.4.1.5 Image Segmentation 111.5 Medical Image Processing Using Python 131.5.1 Medical Image Processing Methods 161.5.1.1 Image Formation 171.5.1.2 Image Enhancement 191.5.1.3 Image Analysis 191.5.1.4 Image Visualization 191.5.1.5 Image Management 191.6 Feature Extraction Using Python 201.7 Case Study on Throat Cancer 241.7.1 Introduction 241.7.1.1 HSI System 251.7.1.2 The Adaptive Deep Learning Method Proposed 251.7.2 Results and Findings 271.7.3 Discussion 281.7.4 Conclusion 291.8 Conclusion 29References 30Additional Reading 31Key Terms and Definition 322 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study 33Shravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar2.1 Introduction 332.2 Literature Review 352.3 Learning Methods 412.3.1 Machine Learning 412.3.2 Deep Learning 422.3.3 Transfer Learning 422.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques 432.4.1 Dataset Description 432.4.2 Evaluation Platform 442.4.3 Training Process 442.4.4 Model Evaluation of CNN Classifier 462.4.5 Mathematical Model 472.4.6 Parameter Optimization 472.4.7 Performance Metrics 502.5 Conclusion 52References 533 Contamination Monitoring System Using IOT and GIS 57Kavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari3.1 Introduction 583.2 Literature Survey 583.3 Proposed Work 603.4 Experimentation and Results 613.4.1 Experimental Setup 613.5 Results 643.6 Conclusion 70Acknowledgement 71References 714 Video Error Concealment Using Particle Swarm Optimization 73Rajani P. K. and Arti Khaparde4.1 Introduction 744.2 Proposed Research Work Overview 754.3 Error Detection 754.4 Frame Replacement Video Error Concealment Algorithm 774.5 Research Methodology 774.5.1 Particle Swarm Optimization 784.5.2 Spatio-Temporal Video Error Concealment Method 784.5.3 Proposed Modified Particle Swarm Optimization Algorithm 794.6 Results and Analysis 834.6.1 Single Frame With Block Error Analysis 854.6.2 Single Frame With Random Error Analysis 864.6.3 Multiple Frame Error Analysis 884.6.4 Sequential Frame Error Analysis 914.6.5 Subjective Video Quality Analysis for Color Videos 934.6.6 Scene Change of Videos 944.7 Conclusion 954.8 Future Scope 97References 975 Enhanced Image Fusion with Guided Filters 99Nalini Jagtap and Sudeep D. Thepade5.1 Introduction 1005.2 Related Works 1005.3 Proposed Methodology 1025.3.1 System Model 1025.3.2 Steps of the Proposed Methodology 1045.4 Experimental Results 1045.4.1 Entropy 1045.4.2 Peak Signal-to-Noise Ratio 1055.4.3 Root Mean Square Error 1075.4.3.1 Qab/f 1085.5 Conclusion 108References 1096 Deepfake Detection Using LSTM-Based Neural Network 111Tejaswini Yesugade, Shrikant Kokate, Sarjana Patil, Ritik Varma and Sejal Pawar6.1 Introduction 1116.2 Related Work 1126.2.1 Deepfake Generation 1126.2.2 LSTM and CNN 1126.3 Existing System 1136.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking 1136.3.2 Detection Using Inconsistence in Head Pose 1136.3.3 Exploiting Visual Artifacts 1136.4 Proposed System 1146.4.1 Dataset 1146.4.2 Preprocessing 1146.4.3 Model 1156.5 Results 1176.6 Limitations 1196.7 Application 1196.8 Conclusion 119References 1197 Classification of Fetal Brain Abnormalities with MRI Images: A Survey 121Kavita Shinde and Anuradha Thakare7.1 Introduction 1217.2 Related Work 1237.3 Evaluation of Related Research 1297.4 General Framework for Fetal Brain Abnormality Classification 1297.4.1 Image Acquisition 1307.4.2 Image Pre-Processing 1307.4.2.1 Image Thresholding 1307.4.2.2 Morphological Operations 1317.4.2.3 Hole Filling and Mask Generation 1317.4.2.4 MRI Segmentation for Fetal Brain Extraction 1327.4.3 Feature Extraction 1327.4.3.1 Gray-Level Co-Occurrence Matrix 1337.4.3.2 Discrete Wavelet Transformation 1337.4.3.3 Gabor Filters 1347.4.3.4 Discrete Statistical Descriptive Features 1347.4.4 Feature Reduction 1347.4.4.1 Principal Component Analysis 1357.4.4.2 Linear Discriminant Analysis 1367.4.4.3 Non-Linear Dimensionality Reduction Techniques 1377.4.5 Classification by Using Machine Learning Classifiers 1377.4.5.1 Support Vector Machine 1387.4.5.2 K-Nearest Neighbors 1387.4.5.3 Random Forest 1397.4.5.4 Linear Discriminant Analysis 1397.4.5.5 Naïve Bayes 1397.4.5.6 Decision Tree (DT) 1407.4.5.7 Convolutional Neural Network 1407.5 Performance Metrics for Research in Fetal Brain Analysis 1417.6 Challenges 1427.7 Conclusion and Future Works 142References 1438 Analysis of COVID-19 Data Using Machine Learning Algorithm 147Chinnaiah Kotadi, Mithun Chakravarthi K., Srihari Chintha and Kapil Gupta8.1 Introduction 1478.2 Pre-Processing 1488.3 Selecting Features 1498.4 Analysis of COVID-19–Confirmed Cases in India 1528.4.1 Analysis to Highest COVID-19–Confirmed Case States in India 1538.4.2 Analysis to Highest COVID-19 Death Rate States in India 1538.4.3 Analysis to Highest COVID-19 Cured Case States in India 1548.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State 1558.5 Linear Regression Used for Predicting Daily Wise COVID- 19Cases in Maharashtra 1568.6 Conclusion 157References 1579 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering 159Manish Sharma and Rutuja Deshmukh9.1 Introduction 1609.2 Related Work 1629.3 Recommender Systems and Collaborative Filtering 1649.4 Proposed Methodology 1659.5 Experiment Analysis 1679.6 Conclusion 168References 16810 Virtual Moratorium System 171Manisha Bhende, Muzasarali Badger, Pranish Kumbhar, Vedanti Bhatkar and Payal Chavan10.1 Introduction 17210.1.1 Objectives 17210.2 Literature Survey 17210.2.1 Virtual Assistant—BLU 17210.2.2 HDFC Ask EVA 17310.3 Methodologies of Problem Solving 17310.4 Modules 17410.4.1 Chatbot 17410.4.2 Android Application 17510.4.3 Web Application 17510.5 Detailed Flow of Proposed Work 17610.5.1 System Architecture 17610.5.2 DFD Level 1 17710.6 Architecture Design 17810.6.1 Main Server 17810.6.2 Chatbot 17810.6.3 Database Architecture 18010.6.4 Web Scraper 18010.7 Algorithms Used 18110.7.1 AES-256 Algorithm 18110.7.2 Rasa NLU 18110.8 Results 18210.9 Discussions 18310.9.1 Applications 18310.9.2 Future Work 18310.9.3 Conclusion 183References 18311 Efficient Land Cover Classification for Urban Planning 185Vandana Tulshidas Chavan and Sanjeev J. Wagh11.1 Introduction 18511.2 Literature Survey 18911.3 Proposed Methodology 19111.4 Conclusion 192References 19212 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review 195Pradnya Patil and Sanjeev J. Wagh12.1 Introduction 19612.2 Literature Survey 19612.3 Problem Statement and Objectives 20112.3.1 Problem Statement 20112.3.2 Objectives 20112.4 Proposed Methodology 20212.4.1 Pre-Processing 20212.4.2 Feature Extraction 20312.4.3 Classification 20312.5 Conclusion 204References 20413 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering 207Anupama Patil, Manisha Bhende, Suvarna Patil and P. P. Shevatekar13.1 Introduction 20813.2 Related Work 21013.3 Distance Measurement Using Stereo Vision 21313.3.1 Calibration of the Camera 21513.3.2 Stereo Image Rectification 21513.3.3 Disparity Estimation and Stereo Matching 21613.3.4 Measurement of Distance 21713.4 Object Segmentation in Depth Map 21813.4.1 Formation of Depth Map 21813.4.2 Density-Based in 3D Object Grouping Clustering 21813.4.3 Layered Images Object Segmentation 21913.4.3.1 Image Layer Formation 22113.4.3.2 Determination of Object Boundaries 22213.5 Conclusion 223References 22414 Real-Time Depth Estimation Using BLOB Detection/ Contour Detection 227Arokia Priya Charles, Anupama V. Patil and Sunil Dambhare14.1 Introduction 22714.2 Estimation of Depth Using Blob Detection 22914.2.1 Grayscale Conversion 23014.2.2 Thresholding 23114.2.3 Image Subtraction in Case of Input with Background 23214.2.3.1 Preliminaries 23314.2.3.2 Computing Time 23414.3 Blob 23414.3.1 BLOB Extraction 23414.3.2 Blob Classification 23514.3.2.1 Image Moments 23614.3.2.2 Centroid Using Image Moments 23814.3.2.3 Central Moments 23814.4 Challenges 24114.5 Experimental Results 24114.6 Conclusion 251References 255Index 257