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|a 006.3/7
|2 23/eng/20230309
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|a Smart computer vision /
|c B. Vinoth Kumar, P. Sivakumar, B. Surendiran, Junhua Ding, editors.
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|a Cham :
|b Springer,
|c [2023]
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|c ©2023
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|a 1 online resource :
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|a EAI/Springer innovations in communication and computing
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|a Includes bibliographical references and index.
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|a 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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| 505 |
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|a 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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|a 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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|a 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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|a 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
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| 533 |
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|a Electronic reproduction.
|b Ann Arbor, MI
|n Available via World Wide Web.
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|a Online resource ; title from PDF title page (EBSCO, viewed March 9, 2023).
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| 650 |
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|a Computer vision.
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| 650 |
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|a Artificial intelligence.
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| 700 |
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|a Kumar, B. Vinoth,
|e editor.
|
| 700 |
1 |
|
|a Sivakumar, P.,
|e editor.
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| 700 |
1 |
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|a Surendiran, B.,
|e editor.
|
| 700 |
1 |
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|a Ding, Junhua,
|e editor.
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| 710 |
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|a ProQuest (Firm)
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| 776 |
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|i Print version:
|a Kumar, B. Vinoth
|t Smart Computer Vision
|d Cham : Springer International Publishing AG,c2023
|z 9783031205408
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|a EAI/Springer innovations in communication and computing.
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