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A guide to convolutional neural netw...

Bennamoun, Mohammed,

A guide to convolutional neural networks for computer vision

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A guide to convolutional neural networks for computer visionSalman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun.
作者:	Khan, Salman.
其他作者:	Rahmani, Hossein,
出版者:	San Rafael, California :Morgan & Claypool Publishers,2018.
面頁冊數:	1 online resource (209 p.)
標題:	Computer visionMathematical models.
電子資源:	click for full text
ISBN:	9781681730219

A guide to convolutional neural networks for computer vision
Khan, Salman.

A guide to convolutional neural networks for computer vision[electronic resource] /Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun. - 1st ed. - San Rafael, California :Morgan & Claypool Publishers,2018. - 1 online resource (209 p.) - Synthesis Lectures on Computer Vision.. - Synthesis Lectures on Computer Vision..

Includes bibliographical references and index.

A guide to convolutional neural networks for computer vision -- Abstract; Keywords -- Contents -- Preface -- Acknowledgments -- 1 Introduction -- 2 Features and Classifiers -- 3 Neural Networks Basics -- 4 Convolutional Neural Network -- 5 CNN Learning -- 6 Examples of CNN Architectures -- 7 Applications of CNNs in Computer Vision -- 8 Deep Learning Tools and Libraries -- 9 Conclusion -- Bibliography -- Authors' Biographies.

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.

ISBN: 9781681730219Subjects--Topical Terms:

206311
Computer vision
--Mathematical models.

LC Class. No.: TA1634 .K424

Dewey Class. No.: 006.37

A guide to convolutional neural networks for computer vision
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520 3 $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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