Guide to Convolutional Neural Networks A Practical Application to Tr
This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work pres
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de to Convolutional Neural Networks A Practical Application to Traffic-Sign Detection and Classification
Guide to Convolutional Neural Networks
Hamed Habibi Aghdam Elnaz Jahani Heravi
Guide to Convolutional Neural Networks A Practical Application to Traffic-Sign Detection and Classification
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Elnaz Jahani Heravi University Rovira i Virgili Tarragona Spain
Hamed Habibi Aghdam University Rovira i Virgili Tarragona Spain
ISBN 978-3-319-57549-0 DOI 10.1007/978-3-319-57550-6
ISBN 978-3-319-57550-6
(eBook)
Library of Congress Control Number: 2017938310 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
To my wife, Elnaz, who possess the most accurate and reliable optimization method and guides me toward global optima of life. Hamed Habibi Aghdam
Preface
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm. Feature extraction methods such as scale invariant feature transform, histogram of oriented gradients, bank of Gabor filters, local binary pattern, bag of features and Fisher vectors are some of the methods that performed well compared with their predecessors. These methods mainly create the feature vector in several steps. For example, scale in
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