{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":22,"n_cols":5,"rows_kept":22,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[11,2,11,3],[12,2,12,3],[15,2,15,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910830692703321"]
[null,null,"Titolo",null,"Object detection by stereo vision images / / edited by R. Arokia Priya [and four others]"]
[null,null,"Pubbl/distr/stampa",null,"Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-119-84228-X"]
[{"$nulls":4},"1-119-84227-1"]
[null,null,"Descrizione fisica",null,"1 online resource (283 pages)"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Computers - Optical equipment"]
[{"$nulls":4},"Optical pattern recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Learning and Deep Learning Techniques: An Analytical Study -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Learning Methods -- 2.3.1 Machine Learning -- 2.3.2 Deep Learning -- 2.3.3 Transfer Learning -- 2.4 Detection of Lung Diseases Using Machine Learning and Deep Learning Techniques -- 2.4.1 Dataset Description -- 2.4.2 Evaluation Platform -- 2.4.3 Training Process -- 2.4.4 Model Evaluation of CNN Classifier -- 2.4.5 Mathematical Model -- 2.4.6 Parameter Optimization -- 2.4.7 Performance Metrics."]
[{"$nulls":4},"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."]
[{"$nulls":4},"Chapter 7 Classification of Fetal Brain Abnormalities with MRI Images: A Survey -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Evaluation of Related Research -- 7.4 General Framework for Fetal Brain Abnormality Classification -- 7.4.1 Image Acquisition -- 7.4.2 Image Pre-Processing -- 7.4.2.1 Image Thresholding -- 7.4.2.2 Morphological Operations -- 7.4.2.3 Hole Filling and Mask Generation -- 7.4.2.4 MRI Segmentation for Fetal Brain Extraction -- 7.4.3 Feature Extraction -- 7.4.3.1 Gray-Level Co-Occurrence Matrix -- 7.4.3.2 Discrete Wavelet Transformation -- 7.4.3.3 Gabor Filters -- 7.4.3.4 Discrete Statistical Descriptive Features -- 7.4.4 Feature Reduction -- 7.4.4.1 Principal Component Analysis -- 7.4.4.2 Linear Discriminant Analysis -- 7.4.4.3 Non-Linear Dimensionality Reduction Techniques -- 7.4.5 Classification by Using Machine Learning Classifiers -- 7.4.5.1 Support Vector Machine -- 7.4.5.2 K-Nearest Neighbors -- 7.4.5.3 Random Forest -- 7.4.5.4 Linear Discriminant Analysis -- 7.4.5.5 Naïve Bayes -- 7.4.5.6 Decision Tree (DT) -- 7.4.5.7 Convolutional Neural Network -- 7.5 Performance Metrics for Research in Fetal Brain Analysis -- 7.6 Challenges -- 7.7 Conclusion and Future Works -- References -- Chapter 8 Analysis of COVID-19 Data Using Machine Learning Algorithm -- 8.1 Introduction -- 8.2 Pre-Processing -- 8.3 Selecting Features -- 8.4 Analysis of COVID-19-Confirmed Cases in India -- "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Chapter 9 Intelligent Recommendation System to Evaluate Teaching Faculty Performance Using Adaptive Collaborative Filtering -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Recommender Systems and Collaborative Filtering -- 9.4 Proposed Methodology -- 9.5 Experiment Analysis -- 9.6 Conclusion -- References -- Chapter 10 Virtual Moratorium System -- 10.1 Introduction -- 10.1.1 Objectives -- 10.2 Literature Survey -- 10.2.1 Virtual Assistant-BLU -- 10.2.2 HDFC Ask EVA -- 10.3 Methodologies of Problem Solving -- 10.4 Modules -- 10.4.1 Chatbot -- 10.4.2 Android Application -- 10.4.3 Web Application -- 10.5 Detailed Flow of Proposed Work -- 10.5.1 System Architecture -- 10.5.2 DFD Level 1 -- 10.6 Architecture Design -- 10.6.1 Main Server -- 10.6.2 Chatbot -- 10.6.3 Database Architecture -- 10.6.4 Web Scraper -- 10.7 Algorithms Used -- 10.7.1 AES-256 Algorithm -- 10.7.2 Rasa NLU -- 10.8 Results -- 10.9 Discussions -- 10.9.1 Applications -- 10.9.2 Future Work -- 10.9.3 Conclusion -- References -- Chapter 11 Efficient Land Cover Classification for Urban Planning -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed Methodology -- 11.4 Conclusion -- References -- Chapter 12 Data-Driven Approches for Fake News Detection on Social Media Platforms: Review -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Problem Statement and Objectives -- 12.3.1 Problem Statement -- 12.3.2 Objectives -- 12.4 Proposed Methodology -- 12.4.1 Pre-Processing -- 12.4.2 Feature Extraction -- 12.4.3 Classification -- 12.5 Conclusion -- References -- Chapter 13 Distance Measurement for Object Detection for Automotive Applications Using 3D Density-Based Clustering -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Distance Measurement Using Stereo Vision -- 13.3.1 Calibration of the Camera -- 13.3.2 Stereo Image Rectification -- 13.3.3 Disparity Estimation and Stereo Matching."]
[{"$nulls":4},"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."]