Intelligent Systems and Applications in Computer Vision:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Milton
Taylor & Francis Group
2023
|
| Ausgabe: | 1st ed. |
| Schlagwörter: | |
| Links: | https://doi.org/10.1201/9781003453406 |
| Abstract: | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- List of Contributors -- Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision -- 1.1 Introduction -- 1.2 Deep Learning Algorithms -- 1.2.1 Convolutional Neural Networks -- 1.2.2 Restricted Boltzmann Machines -- 1.2.3 Deep Boltzmann Machines -- 1.2.4 Deep Belief Networks -- 1.2.5 Stacked (de-Noising) Auto-Encoders -- 1.2.5.1 Auto-Encoders -- 1.2.5.2 Denoising Auto Encoders -- 1.3 Comparison of the Deep Learning Algorithms -- 1.4 Challenges in Deep Learning Algorithms -- 1.5 Conclusion and Future Scope -- References -- Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach -- 2.1 Introduction -- 2.2 Applications of Object Extraction -- 2.3 Edge Detection Techniques -- 2.3.1 Roberts Edge Detection -- 2.3.2 Sobel Edge Detection -- 2.3.3 Prewitt's Operator -- 2.3.4 Laplacian Edge Detection -- 2.4 Related Work -- 2.5 Proposed Model -- 2.6 Results and Discussion -- 2.7 Conclusion -- References -- Chapter 3 Deep Learning Techniques for Image Captioning -- 3.1 Introduction to Image Captioning -- 3.1.1 How Does Image Recognition Work? -- 3.2 Introduction to Deep Learning -- 3.2.1 Pros of the Deep Learning Algorithm -- 3.2.2 Customary / Traditional CV Methodology -- 3.2.3 Limitations/challenges of Traditional CV Methodology -- 3.2.4 Overcome the Limitations of Deep Learning -- 3.3 Deep Learning Algorithms for Object Detection -- 3.3.1 Types of Deep Models for Object Detection -- 3.4 How Image Captioning Works -- 3.4.1 Transformer Based Image Captioning -- 3.4.2 Visual Scene Graph Based Image Captioning -- 3.4.3 Challenges in Image Captioning -- 3.5 Conclusion -- References -- Chapter 4 Deep Learning-Based Object Detection for Computer Vision Tasks: A Survey of Methods and Applications -- 4.1 Introduction. |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781003453406 |
| DOI: | 10.1201/9781003453406 |
Internformat
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| 520 | 3 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- List of Contributors -- Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision -- 1.1 Introduction -- 1.2 Deep Learning Algorithms -- 1.2.1 Convolutional Neural Networks -- 1.2.2 Restricted Boltzmann Machines -- 1.2.3 Deep Boltzmann Machines -- 1.2.4 Deep Belief Networks -- 1.2.5 Stacked (de-Noising) Auto-Encoders -- 1.2.5.1 Auto-Encoders -- 1.2.5.2 Denoising Auto Encoders -- 1.3 Comparison of the Deep Learning Algorithms -- 1.4 Challenges in Deep Learning Algorithms -- 1.5 Conclusion and Future Scope -- References -- Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach -- 2.1 Introduction -- 2.2 Applications of Object Extraction -- 2.3 Edge Detection Techniques -- 2.3.1 Roberts Edge Detection -- 2.3.2 Sobel Edge Detection -- 2.3.3 Prewitt's Operator -- 2.3.4 Laplacian Edge Detection -- 2.4 Related Work -- 2.5 Proposed Model -- 2.6 Results and Discussion -- 2.7 Conclusion -- References -- Chapter 3 Deep Learning Techniques for Image Captioning -- 3.1 Introduction to Image Captioning -- 3.1.1 How Does Image Recognition Work? -- 3.2 Introduction to Deep Learning -- 3.2.1 Pros of the Deep Learning Algorithm -- 3.2.2 Customary / Traditional CV Methodology -- 3.2.3 Limitations/challenges of Traditional CV Methodology -- 3.2.4 Overcome the Limitations of Deep Learning -- 3.3 Deep Learning Algorithms for Object Detection -- 3.3.1 Types of Deep Models for Object Detection -- 3.4 How Image Captioning Works -- 3.4.1 Transformer Based Image Captioning -- 3.4.2 Visual Scene Graph Based Image Captioning -- 3.4.3 Challenges in Image Captioning -- 3.5 Conclusion -- References -- Chapter 4 Deep Learning-Based Object Detection for Computer Vision Tasks: A Survey of Methods and Applications -- 4.1 Introduction. | |
| 653 | 0 | |a Artificial intelligence | |
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Datensatz im Suchindex
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| discipline | Informatik |
| doi_str_mv | 10.1201/9781003453406 |
| edition | 1st ed. |
| format | Electronic eBook |
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| isbn | 9781003453406 |
| language | English |
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| spelling | Mittal, Nitin Verfasser aut Intelligent Systems and Applications in Computer Vision 1st ed. Milton Taylor & Francis Group 2023 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- List of Contributors -- Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision -- 1.1 Introduction -- 1.2 Deep Learning Algorithms -- 1.2.1 Convolutional Neural Networks -- 1.2.2 Restricted Boltzmann Machines -- 1.2.3 Deep Boltzmann Machines -- 1.2.4 Deep Belief Networks -- 1.2.5 Stacked (de-Noising) Auto-Encoders -- 1.2.5.1 Auto-Encoders -- 1.2.5.2 Denoising Auto Encoders -- 1.3 Comparison of the Deep Learning Algorithms -- 1.4 Challenges in Deep Learning Algorithms -- 1.5 Conclusion and Future Scope -- References -- Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach -- 2.1 Introduction -- 2.2 Applications of Object Extraction -- 2.3 Edge Detection Techniques -- 2.3.1 Roberts Edge Detection -- 2.3.2 Sobel Edge Detection -- 2.3.3 Prewitt's Operator -- 2.3.4 Laplacian Edge Detection -- 2.4 Related Work -- 2.5 Proposed Model -- 2.6 Results and Discussion -- 2.7 Conclusion -- References -- Chapter 3 Deep Learning Techniques for Image Captioning -- 3.1 Introduction to Image Captioning -- 3.1.1 How Does Image Recognition Work? -- 3.2 Introduction to Deep Learning -- 3.2.1 Pros of the Deep Learning Algorithm -- 3.2.2 Customary / Traditional CV Methodology -- 3.2.3 Limitations/challenges of Traditional CV Methodology -- 3.2.4 Overcome the Limitations of Deep Learning -- 3.3 Deep Learning Algorithms for Object Detection -- 3.3.1 Types of Deep Models for Object Detection -- 3.4 How Image Captioning Works -- 3.4.1 Transformer Based Image Captioning -- 3.4.2 Visual Scene Graph Based Image Captioning -- 3.4.3 Challenges in Image Captioning -- 3.5 Conclusion -- References -- Chapter 4 Deep Learning-Based Object Detection for Computer Vision Tasks: A Survey of Methods and Applications -- 4.1 Introduction. Artificial intelligence Computational intelligence Kant Pandit, Amit ctb Abouhawwash, Mohamed ctb Mahajan, Shubham ctb Erscheint auch als Druck-Ausgabe 9781032591872 Erscheint auch als Druck-Ausgabe 978-1-000-98586-3 https://doi.org/10.1201/9781003453406 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Mittal, Nitin Intelligent Systems and Applications in Computer Vision |
| title | Intelligent Systems and Applications in Computer Vision |
| title_auth | Intelligent Systems and Applications in Computer Vision |
| title_exact_search | Intelligent Systems and Applications in Computer Vision |
| title_full | Intelligent Systems and Applications in Computer Vision |
| title_fullStr | Intelligent Systems and Applications in Computer Vision |
| title_full_unstemmed | Intelligent Systems and Applications in Computer Vision |
| title_short | Intelligent Systems and Applications in Computer Vision |
| title_sort | intelligent systems and applications in computer vision |
| url | https://doi.org/10.1201/9781003453406 |
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