A Guide to Convolutional Neural Networks for Computer Vision / by Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun.

Format:

Book

Author/Creator:

Khan, Salman., Author.

Rahmani, Hossein., Author.

Shah, Syed Afaq Ali., Author.

Bennamoun, Mohammed., Author.

Series:

Synthesis Lectures on Computer Vision, 2153-1064

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Pattern recognition systems.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Automated Pattern Recognition.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Automated Pattern Recognition.

Physical Description:

1 online resource (XIX, 187 p.)

Edition:

1st ed. 2018.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

Mode of access: World Wide Web.

Summary:

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.

Contents:

Preface

Acknowledgments

Introduction

Features and Classifiers

Neural Networks Basics

Convolutional Neural Network

CNN Learning

Examples of CNN Architectures

Applications of CNNs in Computer Vision

Deep Learning Tools and Libraries

Conclusion

Bibliography

Authors' Biographies.

Notes:

Includes bibliographical references and index.

ISBN:

9783031018213

3031018214