A Guide to Convolutional Neural Networks for Computer Vision_Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun.pdf

AGuideto ConvolutionalNeuralNetworks forComputerVision Salman Khan Data61-CSIRO and Australian National University Hossein Rahmani The University of Western Australia, Crawley, WA Syed Afaq Ali Shah The University of Western Australia, Crawley, WA Mohammed Bennamoun The University of Western Australia, Crawley, WA SYNTHESIS LECTURES ON COMPUTER VISION #15C M & cLaypoo lMorgan publishers& Copyright © 2018 by Morgan & Claypool A Guide to Convolutional Neural Networks for Computer Vision Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, and Mohammed Bennamoun www.morganclaypool.com ISBN: 9781681730219 paperback ISBN: 9781681730226 ebook ISBN: 9781681732787 hardcover DOI 10.2200/S00822ED1V01Y201712COV015 A Publication in the Morgan & Claypool Publishers series SYNTHESIS LECTURES ON COMPUTER VISION Lecture #15 Series Editors: Gérard Medioni,University of Southern California Sven Dickinson,University of Toronto Series ISSN Print 2153-1056 Electronic 2153-1064 ABSTRACT 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 be- hind 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 r