{"product_id":"object-detection-by-stereo-vision-images-hardback-9781119842194","title":"Object Detection by Stereo Vision Images (Hardback) 9781119842194","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eObject Detection by Stereo Vision Images\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eR. Arokia Priya (Edited by), RA Priya (Author), Anupama V. Patil (Edited by), Manisha Bhende (Edited by), Anuradha D. Thakare (Edited by), Sanjeev Wagh (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119842194, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 6 November 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e288 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eOBJECT DETECTION BY STEREO VISION IMAGES\u003c\/b\u003e \u003cp\u003e\u003cb\u003eSince 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eCurrent 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. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e Audience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern \u0026amp; data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Conditioning for Medical Imaging 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShahzia Sayyad, Deepti Nikumbh, Dhruvi Lalit Jain, Prachi Dhiren Khatri, Alok Saratchandra Panda and Rupesh Ravindra Joshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Importance of Image Preprocessing 2\u003c\/p\u003e \u003cp\u003e1.3 Introduction to Digital Medical Imaging 3\u003c\/p\u003e \u003cp\u003e1.3.1 Types of Medical Images for Screening 4\u003c\/p\u003e \u003cp\u003e1.3.1.1 X-rays 4\u003c\/p\u003e \u003cp\u003e1.3.1.2 Computed Tomography (CT) Scan 4\u003c\/p\u003e \u003cp\u003e1.3.1.3 Ultrasound 4\u003c\/p\u003e \u003cp\u003e1.3.1.4 Magnetic Resonance Imaging (MRI) 5\u003c\/p\u003e \u003cp\u003e1.3.1.5 Positron Emission Tomography (PET) Scan 5\u003c\/p\u003e \u003cp\u003e1.3.1.6 Mammogram 5\u003c\/p\u003e \u003cp\u003e1.3.1.7 Fluoroscopy 5\u003c\/p\u003e \u003cp\u003e1.3.1.8 Infrared Thermography 6\u003c\/p\u003e \u003cp\u003e1.4 Preprocessing Techniques of Medical Imaging Using Python 6\u003c\/p\u003e \u003cp\u003e1.4.1 Medical Image Preprocessing 6\u003c\/p\u003e \u003cp\u003e1.4.1.1 Reading the Image 7\u003c\/p\u003e \u003cp\u003e1.4.1.2 Resizing the Image 7\u003c\/p\u003e \u003cp\u003e1.4.1.3 Noise Removal 8\u003c\/p\u003e \u003cp\u003e1.4.1.4 Filtering and Smoothing 9\u003c\/p\u003e \u003cp\u003e1.4.1.5 Image Segmentation 11\u003c\/p\u003e \u003cp\u003e1.5 Medical Image Processing Using Python 13\u003c\/p\u003e \u003cp\u003e1.5.1 Medical Image Processing Methods 16\u003c\/p\u003e \u003cp\u003e1.5.1.1 Image Formation 17\u003c\/p\u003e \u003cp\u003e1.5.1.2 Image Enhancement 19\u003c\/p\u003e \u003cp\u003e1.5.1.3 Image Analysis 19\u003c\/p\u003e \u003cp\u003e1.5.1.4 Image Visualization 19\u003c\/p\u003e \u003cp\u003e1.5.1.5 Image Management 19\u003c\/p\u003e \u003cp\u003e1.6 Feature Extraction Using Python 20\u003c\/p\u003e \u003cp\u003e1.7 Case Study on Throat Cancer 24\u003c\/p\u003e \u003cp\u003e1.7.1 Introduction 24\u003c\/p\u003e \u003cp\u003e1.7.1.1 HSI System 25\u003c\/p\u003e \u003cp\u003e1.7.1.2 The Adaptive Deep Learning Method Proposed 25\u003c\/p\u003e \u003cp\u003e1.7.2 Results and Findings 27\u003c\/p\u003e \u003cp\u003e1.7.3 Discussion 28\u003c\/p\u003e \u003cp\u003e1.7.4 Conclusion 29\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 29\u003c\/p\u003e \u003cp\u003eReferences 30\u003c\/p\u003e \u003cp\u003eAdditional Reading 31\u003c\/p\u003e \u003cp\u003eKey Terms and Definition 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Detection of Pneumonia Using Machine Learning and Deep Learning Techniques: An Analytical Study 33\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShravani Nimbolkar, Anuradha Thakare, Subhradeep Mitra, Omkar Biranje and Anant Sutar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 33\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 35\u003c\/p\u003e \u003cp\u003e2.3 Learning Methods 41\u003c\/p\u003e \u003cp\u003e2.3.1 Machine Learning 41\u003c\/p\u003e \u003cp\u003e2.3.2 Deep Learning 42\u003c\/p\u003e \u003cp\u003e2.3.3 Transfer Learning 42\u003c\/p\u003e \u003cp\u003e2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques 43\u003c\/p\u003e \u003cp\u003e2.4.1 Dataset Description 43\u003c\/p\u003e \u003cp\u003e2.4.2 Evaluation Platform 44\u003c\/p\u003e \u003cp\u003e2.4.3 Training Process 44\u003c\/p\u003e \u003cp\u003e2.4.4 Model Evaluation of CNN Classifier 46\u003c\/p\u003e \u003cp\u003e2.4.5 Mathematical Model 47\u003c\/p\u003e \u003cp\u003e2.4.6 Parameter Optimization 47\u003c\/p\u003e \u003cp\u003e2.4.7 Performance Metrics 50\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 52\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Contamination Monitoring System Using IOT and GIS 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKavita R. Singh, Ravi Wasalwar, Ajit Dharmik and Deepshikha Tiwari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 58\u003c\/p\u003e \u003cp\u003e3.2 Literature Survey 58\u003c\/p\u003e \u003cp\u003e3.3 Proposed Work 60\u003c\/p\u003e \u003cp\u003e3.4 Experimentation and Results 61\u003c\/p\u003e \u003cp\u003e3.4.1 Experimental Setup 61\u003c\/p\u003e \u003cp\u003e3.5 Results 64\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 70\u003c\/p\u003e \u003cp\u003eAcknowledgement 71\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Video Error Concealment Using Particle Swarm Optimization 73\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajani P. K. and Arti Khaparde\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 74\u003c\/p\u003e \u003cp\u003e4.2 Proposed Research Work Overview 75\u003c\/p\u003e \u003cp\u003e4.3 Error Detection 75\u003c\/p\u003e \u003cp\u003e4.4 Frame Replacement Video Error Concealment Algorithm 77\u003c\/p\u003e \u003cp\u003e4.5 Research Methodology 77\u003c\/p\u003e \u003cp\u003e4.5.1 Particle Swarm Optimization 78\u003c\/p\u003e \u003cp\u003e4.5.2 Spatio-Temporal Video Error Concealment Method 78\u003c\/p\u003e \u003cp\u003e4.5.3 Proposed Modified Particle Swarm Optimization Algorithm 79\u003c\/p\u003e \u003cp\u003e4.6 Results and Analysis 83\u003c\/p\u003e \u003cp\u003e4.6.1 Single Frame With Block Error Analysis 85\u003c\/p\u003e \u003cp\u003e4.6.2 Single Frame With Random Error Analysis 86\u003c\/p\u003e \u003cp\u003e4.6.3 Multiple Frame Error Analysis 88\u003c\/p\u003e \u003cp\u003e4.6.4 Sequential Frame Error Analysis 91\u003c\/p\u003e \u003cp\u003e4.6.5 Subjective Video Quality Analysis for Color Videos 93\u003c\/p\u003e \u003cp\u003e4.6.6 Scene Change of Videos 94\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 95\u003c\/p\u003e \u003cp\u003e4.8 Future Scope 97\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Enhanced Image Fusion with Guided Filters 99\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNalini Jagtap and Sudeep D. Thepade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 100\u003c\/p\u003e \u003cp\u003e5.2 Related Works 100\u003c\/p\u003e \u003cp\u003e5.3 Proposed Methodology 102\u003c\/p\u003e \u003cp\u003e5.3.1 System Model 102\u003c\/p\u003e \u003cp\u003e5.3.2 Steps of the Proposed Methodology 104\u003c\/p\u003e \u003cp\u003e5.4 Experimental Results 104\u003c\/p\u003e \u003cp\u003e5.4.1 Entropy 104\u003c\/p\u003e \u003cp\u003e5.4.2 Peak Signal-to-Noise Ratio 105\u003c\/p\u003e \u003cp\u003e5.4.3 Root Mean Square Error 107\u003c\/p\u003e \u003cp\u003e5.4.3.1 Qab\/f 108\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 108\u003c\/p\u003e \u003cp\u003eReferences 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Deepfake Detection Using LSTM-Based Neural Network 111\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTejaswini Yesugade, Shrikant Kokate, Sarjana Patil, Ritik Varma and Sejal Pawar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 111\u003c\/p\u003e \u003cp\u003e6.2 Related Work 112\u003c\/p\u003e \u003cp\u003e6.2.1 Deepfake Generation 112\u003c\/p\u003e \u003cp\u003e6.2.2 LSTM and CNN 112\u003c\/p\u003e \u003cp\u003e6.3 Existing System 113\u003c\/p\u003e \u003cp\u003e6.3.1 AI-Generated Fake Face Videos by Detecting Eye Blinking 113\u003c\/p\u003e \u003cp\u003e6.3.2 Detection Using Inconsistence in Head Pose 113\u003c\/p\u003e \u003cp\u003e6.3.3 Exploiting Visual Artifacts 113\u003c\/p\u003e \u003cp\u003e6.4 Proposed System 114\u003c\/p\u003e \u003cp\u003e6.4.1 Dataset 114\u003c\/p\u003e \u003cp\u003e6.4.2 Preprocessing 114\u003c\/p\u003e \u003cp\u003e6.4.3 Model 115\u003c\/p\u003e \u003cp\u003e6.5 Results 117\u003c\/p\u003e \u003cp\u003e6.6 Limitations 119\u003c\/p\u003e \u003cp\u003e6.7 Application 119\u003c\/p\u003e \u003cp\u003e6.8 Conclusion 119\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey 121\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKavita Shinde and Anuradha Thakare\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 121\u003c\/p\u003e \u003cp\u003e7.2 Related Work 123\u003c\/p\u003e \u003cp\u003e7.3 Evaluation of Related Research 129\u003c\/p\u003e \u003cp\u003e7.4 General Framework for Fetal Brain Abnormality Classification 129\u003c\/p\u003e \u003cp\u003e7.4.1 Image Acquisition 130\u003c\/p\u003e \u003cp\u003e7.4.2 Image Pre-Processing 130\u003c\/p\u003e \u003cp\u003e7.4.2.1 Image Thresholding 130\u003c\/p\u003e \u003cp\u003e7.4.2.2 Morphological Operations 131\u003c\/p\u003e \u003cp\u003e7.4.2.3 Hole Filling and Mask Generation 131\u003c\/p\u003e \u003cp\u003e7.4.2.4 MRI Segmentation for Fetal Brain Extraction 132\u003c\/p\u003e \u003cp\u003e7.4.3 Feature Extraction 132\u003c\/p\u003e \u003cp\u003e7.4.3.1 Gray-Level Co-Occurrence Matrix 133\u003c\/p\u003e \u003cp\u003e7.4.3.2 Discrete Wavelet Transformation 133\u003c\/p\u003e \u003cp\u003e7.4.3.3 Gabor Filters 134\u003c\/p\u003e \u003cp\u003e7.4.3.4 Discrete Statistical Descriptive Features 134\u003c\/p\u003e \u003cp\u003e7.4.4 Feature Reduction 134\u003c\/p\u003e \u003cp\u003e7.4.4.1 Principal Component Analysis 135\u003c\/p\u003e \u003cp\u003e7.4.4.2 Linear Discriminant Analysis 136\u003c\/p\u003e \u003cp\u003e7.4.4.3 Non-Linear Dimensionality Reduction Techniques 137\u003c\/p\u003e \u003cp\u003e7.4.5 Classification by Using Machine Learning Classifiers 137\u003c\/p\u003e \u003cp\u003e7.4.5.1 Support Vector Machine 138\u003c\/p\u003e \u003cp\u003e7.4.5.2 K-Nearest Neighbors 138\u003c\/p\u003e \u003cp\u003e7.4.5.3 Random Forest 139\u003c\/p\u003e \u003cp\u003e7.4.5.4 Linear Discriminant Analysis 139\u003c\/p\u003e \u003cp\u003e7.4.5.5 Naïve Bayes 139\u003c\/p\u003e \u003cp\u003e7.4.5.6 Decision Tree (DT) 140\u003c\/p\u003e \u003cp\u003e7.4.5.7 Convolutional Neural Network 140\u003c\/p\u003e \u003cp\u003e7.5 Performance Metrics for Research in Fetal Brain Analysis 141\u003c\/p\u003e \u003cp\u003e7.6 Challenges 142\u003c\/p\u003e \u003cp\u003e7.7 Conclusion and Future Works 142\u003c\/p\u003e \u003cp\u003eReferences 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Analysis of COVID-19 Data Using Machine Learning Algorithm 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChinnaiah Kotadi, Mithun Chakravarthi K., Srihari Chintha and Kapil Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 147\u003c\/p\u003e \u003cp\u003e8.2 Pre-Processing 148\u003c\/p\u003e \u003cp\u003e8.3 Selecting Features 149\u003c\/p\u003e \u003cp\u003e8.4 Analysis of COVID-19–Confirmed Cases in India 152\u003c\/p\u003e \u003cp\u003e8.4.1 Analysis to Highest COVID-19–Confirmed Case States in India 153\u003c\/p\u003e \u003cp\u003e8.4.2 Analysis to Highest COVID-19 Death Rate States in India 153\u003c\/p\u003e \u003cp\u003e8.4.3 Analysis to Highest COVID-19 Cured Case States in India 154\u003c\/p\u003e \u003cp\u003e8.4.4 Analysis of Daily COVID-19 Cases in Maharashtra State 155\u003c\/p\u003e \u003cp\u003e8.5 Linear Regression Used for Predicting Daily Wise COVID- 19\u003c\/p\u003e \u003cp\u003eCases in Maharashtra 156\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 157\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering 159\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManish Sharma and Rutuja Deshmukh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 160\u003c\/p\u003e \u003cp\u003e9.2 Related Work 162\u003c\/p\u003e \u003cp\u003e9.3 Recommender Systems and Collaborative Filtering 164\u003c\/p\u003e \u003cp\u003e9.4 Proposed Methodology 165\u003c\/p\u003e \u003cp\u003e9.5 Experiment Analysis 167\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 168\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Virtual Moratorium System 171\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManisha Bhende, Muzasarali Badger, Pranish Kumbhar, Vedanti Bhatkar and Payal Chavan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 172\u003c\/p\u003e \u003cp\u003e10.1.1 Objectives 172\u003c\/p\u003e \u003cp\u003e10.2 Literature Survey 172\u003c\/p\u003e \u003cp\u003e10.2.1 Virtual Assistant—BLU 172\u003c\/p\u003e \u003cp\u003e10.2.2 HDFC Ask EVA 173\u003c\/p\u003e \u003cp\u003e10.3 Methodologies of Problem Solving 173\u003c\/p\u003e \u003cp\u003e10.4 Modules 174\u003c\/p\u003e \u003cp\u003e10.4.1 Chatbot 174\u003c\/p\u003e \u003cp\u003e10.4.2 Android Application 175\u003c\/p\u003e \u003cp\u003e10.4.3 Web Application 175\u003c\/p\u003e \u003cp\u003e10.5 Detailed Flow of Proposed Work 176\u003c\/p\u003e \u003cp\u003e10.5.1 System Architecture 176\u003c\/p\u003e \u003cp\u003e10.5.2 DFD Level 1 177\u003c\/p\u003e \u003cp\u003e10.6 Architecture Design 178\u003c\/p\u003e \u003cp\u003e10.6.1 Main Server 178\u003c\/p\u003e \u003cp\u003e10.6.2 Chatbot 178\u003c\/p\u003e \u003cp\u003e10.6.3 Database Architecture 180\u003c\/p\u003e \u003cp\u003e10.6.4 Web Scraper 180\u003c\/p\u003e \u003cp\u003e10.7 Algorithms Used 181\u003c\/p\u003e \u003cp\u003e10.7.1 AES-256 Algorithm 181\u003c\/p\u003e \u003cp\u003e10.7.2 Rasa NLU 181\u003c\/p\u003e \u003cp\u003e10.8 Results 182\u003c\/p\u003e \u003cp\u003e10.9 Discussions 183\u003c\/p\u003e \u003cp\u003e10.9.1 Applications 183\u003c\/p\u003e \u003cp\u003e10.9.2 Future Work 183\u003c\/p\u003e \u003cp\u003e10.9.3 Conclusion 183\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Efficient Land Cover Classification for Urban Planning 185\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVandana Tulshidas Chavan and Sanjeev J. Wagh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 185\u003c\/p\u003e \u003cp\u003e11.2 Literature Survey 189\u003c\/p\u003e \u003cp\u003e11.3 Proposed Methodology 191\u003c\/p\u003e \u003cp\u003e11.4 Conclusion 192\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review 195\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePradnya Patil and Sanjeev J. Wagh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 196\u003c\/p\u003e \u003cp\u003e12.2 Literature Survey 196\u003c\/p\u003e \u003cp\u003e12.3 Problem Statement and Objectives 201\u003c\/p\u003e \u003cp\u003e12.3.1 Problem Statement 201\u003c\/p\u003e \u003cp\u003e12.3.2 Objectives 201\u003c\/p\u003e \u003cp\u003e12.4 Proposed Methodology 202\u003c\/p\u003e \u003cp\u003e12.4.1 Pre-Processing 202\u003c\/p\u003e \u003cp\u003e12.4.2 Feature Extraction 203\u003c\/p\u003e \u003cp\u003e12.4.3 Classification 203\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 204\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnupama Patil, Manisha Bhende, Suvarna Patil and P. P. Shevatekar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 208\u003c\/p\u003e \u003cp\u003e13.2 Related Work 210\u003c\/p\u003e \u003cp\u003e13.3 Distance Measurement Using Stereo Vision 213\u003c\/p\u003e \u003cp\u003e13.3.1 Calibration of the Camera 215\u003c\/p\u003e \u003cp\u003e13.3.2 Stereo Image Rectification 215\u003c\/p\u003e \u003cp\u003e13.3.3 Disparity Estimation and Stereo Matching 216\u003c\/p\u003e \u003cp\u003e13.3.4 Measurement of Distance 217\u003c\/p\u003e \u003cp\u003e13.4 Object Segmentation in Depth Map 218\u003c\/p\u003e \u003cp\u003e13.4.1 Formation of Depth Map 218\u003c\/p\u003e \u003cp\u003e13.4.2 Density-Based in 3D Object Grouping Clustering 218\u003c\/p\u003e \u003cp\u003e13.4.3 Layered Images Object Segmentation 219\u003c\/p\u003e \u003cp\u003e13.4.3.1 Image Layer Formation 221\u003c\/p\u003e \u003cp\u003e13.4.3.2 Determination of Object Boundaries 222\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 223\u003c\/p\u003e \u003cp\u003eReferences 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Real-Time Depth Estimation Using BLOB Detection\/ Contour Detection 227\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArokia Priya Charles, Anupama V. Patil and Sunil Dambhare\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 227\u003c\/p\u003e \u003cp\u003e14.2 Estimation of Depth Using Blob Detection 229\u003c\/p\u003e \u003cp\u003e14.2.1 Grayscale Conversion 230\u003c\/p\u003e \u003cp\u003e14.2.2 Thresholding 231\u003c\/p\u003e \u003cp\u003e14.2.3 Image Subtraction in Case of Input with Background 232\u003c\/p\u003e \u003cp\u003e14.2.3.1 Preliminaries 233\u003c\/p\u003e \u003cp\u003e14.2.3.2 Computing Time 234\u003c\/p\u003e \u003cp\u003e14.3 Blob 234\u003c\/p\u003e \u003cp\u003e14.3.1 BLOB Extraction 234\u003c\/p\u003e \u003cp\u003e14.3.2 Blob Classification 235\u003c\/p\u003e \u003cp\u003e14.3.2.1 Image Moments 236\u003c\/p\u003e \u003cp\u003e14.3.2.2 Centroid Using Image Moments 238\u003c\/p\u003e \u003cp\u003e14.3.2.3 Central Moments 238\u003c\/p\u003e \u003cp\u003e14.4 Challenges 241\u003c\/p\u003e \u003cp\u003e14.5 Experimental Results 241\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 251\u003c\/p\u003e \u003cp\u003eReferences 255\u003c\/p\u003e \u003cp\u003eIndex 257\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer programming \/ software development [\u003ca title=\"See our other books on Computer programming \/ software development\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20programming%20\/%20software%20development%20%5BUM%5D%22\"\u003eUM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430976385304,"sku":"9781119842194","price":115.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119842194.jpg?v=1784767181","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/object-detection-by-stereo-vision-images-hardback-9781119842194","provider":"Freshly Printed Books","version":"1.0","type":"link"}