| Description |
1 online resource : illustrations (some color) |
| Series |
EAI/Springer innovations in communication and computing |
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EAI/Springer innovations in communication and computing.
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| Contents |
Intro -- Preface -- Contents -- A Systematic Review on Machine Learning-Based Sports Video Summarization Techniques -- 1 Introduction -- 2 Two Decades of Research in Sports Video Summarization -- 2.1 Feature-Based Approaches -- 2.2 Cluster-Based Approaches -- 2.3 Excitement-Based Approaches -- 2.4 Key Event-Based Approaches -- 2.5 Object Detection -- 2.6 Performance Metrics -- 2.6.1 Objective Metrics -- 2.6.2 Subjective Metrics Based on User Experience -- 3 Evolution of Ideas, Algorithms, and Methods for Sports Video Summarization -- 4 Scope for Future Research in Video Summarization |
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4.1 Common Weaknesses of Existing Methods -- 4.1.1 Audio-Based Methods -- 4.1.2 Shot and Boundary Detection -- 4.1.3 Resolution and Samples -- 4.1.4 Events Detection -- 4.2 Scope for Further Research -- 5 Conclusion -- References -- Shot Boundary Detection from Lecture Video Sequences Using Histogram of Oriented Gradients and Radiometric Correlation -- 1 Introduction -- 2 Shot Boundary Detection and Key Frame Extraction -- 2.1 Feature Extraction -- 2.2 Radiometric Correlation for Interframe Similarity Measure -- 2.3 Entropic Measure for Distinguishing Shot Transitions -- 2.4 Key Frame Extraction |
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3 Results and Discussions -- 3.1 Analysis of Results -- 3.2 Discussions and Future Works -- 4 Conclusions -- References -- Detection of Road Potholes Using Computer Vision and Machine Learning Approaches to Assist the Visually Challenged -- 1 Introduction -- 2 Related Works -- 3 Methodologies -- 3.1 Pothole Detection Using Machine Learning and Computer Vision -- 3.2 Pothole Detection Using Deep Learning Model -- 4 Implementation -- 5 Result Analysis -- 6 Conclusion -- References -- Shape Feature Extraction Techniques for Computer VisionApplications -- 1 Introduction -- 2 Feature Extraction |
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3 Various Techniques in Feature Extraction -- 3.1 Histograms of Edge Directions -- 3.2 This Harris Corner -- 3.3 Scale-Invariant Feature Transform -- 3.4 Eigenvector Approaches -- 3.5 Angular Radial Partitioning -- 3.6 Edge Pixel Neighborhood Information -- 3.7 Color Histograms -- 3.8 Edge Histogram Descriptor -- 3.9 Shape Descriptor -- 4 Shape Signature -- 4.1 Centroid Distance Function -- 4.2 Chord Length Function -- 4.3 . Area Function -- 5 Real-Time Applications of Shape Feature Extraction and Object Recognition -- 5.1 Fruit Recognition -- 5.2 Leaf Recognition 2 -- 5.3 Object Recognition |
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6 Recent Works -- 7 Summary and Conclusion -- References -- GLCM Feature-Based Texture Image Classification Using Machine Learning Algorithms -- 1 Introduction -- 2 GLCM -- 2.1 Computation of GLCM Matrix -- 2.2 GLCM Features -- 2.2.1 Energy -- 2.2.2 Entropy -- 2.2.3 Sum Entropy -- 2.2.4 Difference Entropy -- 2.2.5 Contrast -- 2.2.6 Variance -- 2.2.7 Sum Variance -- 2.2.8 Difference Variance -- 2.2.9 Local Homogeneity or Inverse Difference Moment (IDM) -- 2.2.10 Local Homogeneity or Inverse Difference Moment (IDM) -- 2.2.11 RMS Contrast -- 2.2.12 Cluster Shade -- 2.2.13 Cluster Prominence |
| Summary |
This book addresses and disseminates research and development in the applications of intelligent techniques for computer vision, the field that works on enabling computers to see, identify, and process images in the same way that human vision does, and then providing appropriate output. The book provides contributions which include theory, case studies, and intelligent techniques pertaining to computer vision applications. The book helps readers grasp the essence of the recent advances in this complex field. The audience includes researchers, professionals, practitioners, and students from academia and industry who work in this interdisciplinary field. The authors aim to inspire future research both from theoretical and practical viewpoints to spur further advances in the field. Introduction to theoretical foundations and practical solution techniques for computer vision applications; Includes a variety of case studies emphasizing social and research perspectives in computer vision; Features contributors form industry, academia, and researchers with a variety of perspectives |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource ; title from PDF title page (EBSCO, viewed March 9, 2023) |
| Subject |
Computer vision.
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Artificial intelligence.
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artificial intelligence.
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Artificial intelligence
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Computer vision
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| Form |
Electronic book
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| Author |
Kumar, B. Vinoth, editor.
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Sivakumar, P., editor
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Surendiran, B., editor.
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Ding, Junhua, editor.
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| ISBN |
9783031205415 |
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3031205413 |
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