Computer Vision Metrics Textbook Edition
Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well
- PDF / 27,447,149 Bytes
- 653 Pages / 504.63 x 737.01 pts Page_size
- 54 Downloads / 412 Views
Computer Vision Metrics Textbook Edition Survey, Taxonomy and Analysis of Computer Vision, Visual Neuroscience, and Deep Learning
Computer Vision Metrics
Scott Krig
Computer Vision Metrics Textbook Edition Survey, Taxonomy and Analysis of Computer Vision, Visual Neuroscience, and Deep Learning
Scott Krig Krig Research, USA
ISBN 978-3-319-33761-6 ISBN 978-3-319-33762-3 (eBook) DOI 10.1007/978-3-319-33762-3 Library of Congress Control Number: 2016938637 # Springer International Publishing Switzerland 2016 This Springer imprint is published by Springer NatureThis 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. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
Foreword to the Second Edition
The goal of this second version is to add new materials on deep learning, neuroscience applied to computer vision, historical developments in neural networks, and feature learning architectures, particularly neural network methods. In addition, this second edition cleans up some typos and other items from the first version. In total, three new chapters are added to survey the latest feature learning, and hierarchical deep learning methods and architectures. Overall, this book is provides a wide survey of computer vision methods including local feature descriptors, regional and global features, and feature learning methods, with a taxonomy for organizational purposes. Analysis is distributed through the book to provide intuition behind the various approaches, encouraging the reader to think for themselves about the motivations for each approach, why different methods are created, how each method is designed and architected, and why it works. Nearly 1000 references to the literature and other materials are provided, making computer vision and imaging resources accessible at many levels. My expectation for the reader is this: if you want to learn about 90 % of computer vision, read thi
