A guide to convolutional neural networks for computer vision:
Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
San Rafael, California
Morgan & Claypool
2018
|
| Schriftenreihe: | Synthesis lectures on computer vision
#15 |
| Schlagwörter: | |
| Links: | https://www.doi.org/10.1007/978-3-031-01821-3 https://doi.org/10.1007/978-3-031-01821-3 https://doi.org/10.2200/S00822ED1V01Y201712COV015 |
| Zusammenfassung: | Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models |
| Beschreibung: | Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on February 24, 2018) |
| Umfang: | 1 Online-Resource (xix, 187 Seiten) Illustrationen |
| ISBN: | 9781681730226 9783031018213 |
| DOI: | 10.2200/S00822ED1V01Y201712COV015 |
Internformat
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| 520 | |a Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models | ||
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| discipline | Informatik |
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| indexdate | 2024-12-20T18:51:57Z |
| institution | BVB |
| isbn | 9781681730226 9783031018213 |
| language | English |
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| spelling | Khan, Salman Verfasser (DE-588)1033202517 aut A guide to convolutional neural networks for computer vision Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun San Rafael, California Morgan & Claypool 2018 1 Online-Resource (xix, 187 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on computer vision #15 Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on February 24, 2018) Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models Computer vision Mathematical models Neural networks (Computer science) Convolutions (Mathematics) Rahmani, Hossein Sonstige (DE-588)1155310853 oth Shah, Syed Afaq Ali Sonstige (DE-588)1155310950 oth Bennamoun, Mohammed 1961- Sonstige (DE-588)123121795 oth Erscheint auch als Druck-Ausgabe 9781681730219 9781681732787 978-3-031-00693-7 978-3-031-00078-2 Synthesis lectures on computer vision #15 (DE-604)BV048379416 15 https://doi.org/10.2200/S00822ED1V01Y201712COV015 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Khan, Salman A guide to convolutional neural networks for computer vision Synthesis lectures on computer vision Computer vision Mathematical models Neural networks (Computer science) Convolutions (Mathematics) |
| title | A guide to convolutional neural networks for computer vision |
| title_auth | A guide to convolutional neural networks for computer vision |
| title_exact_search | A guide to convolutional neural networks for computer vision |
| title_full | A guide to convolutional neural networks for computer vision Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun |
| title_fullStr | A guide to convolutional neural networks for computer vision Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun |
| title_full_unstemmed | A guide to convolutional neural networks for computer vision Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun |
| title_short | A guide to convolutional neural networks for computer vision |
| title_sort | a guide to convolutional neural networks for computer vision |
| topic | Computer vision Mathematical models Neural networks (Computer science) Convolutions (Mathematics) |
| topic_facet | Computer vision Mathematical models Neural networks (Computer science) Convolutions (Mathematics) |
| url | https://doi.org/10.2200/S00822ED1V01Y201712COV015 |
| volume_link | (DE-604)BV048379416 |
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