Mahmoud Hassaballah - Deep Learning in Computer Vision: Principles and Applications

Deep Learning in Computer Vision

Digital Imaging and Computer Vision Series

Series Editor
Rastislav Lukac
Foveon, Inc./Sigma Corporation San Jose, California, U.S.A.

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Deep Learning in Computer Vision

Principles and Applications

Edited by

Mahmoud Hassaballah and Ali Ismail Awad

CRC Press
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2020 by Taylor & Francis Group, LLC
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Library of Congress Cataloging-in-Publication Data

Names: Hassaballah, Mahmoud, editor. | Awad, Ali Ismail, editor.

Title: Deep learning in computer vision : principles and applications / edited by M. Hassaballah and Ali Ismail Awad.

Description: First edition. | Boca Raton, FL : CRC Press/Taylor and Francis, 2020. | Series: Digital imaging and computer vision | Includes bibliographical references and index.

Identifiers: LCCN 2019057832 (print) | LCCN 2019057833 (ebook) | ISBN 9781138544420 (hardback ; acid-free paper) | ISBN 9781351003827 (ebook)

Subjects: LCSH: Computer vision. | Machine learning.

Classification: LCC TA1634 .D437 2020 (print) | LCC TA1634 (ebook) | DDC 006.3/7--dc23

LC record available at https://lccn.loc.gov/2019057832

LC ebook record available at https://lccn.loc.gov/2019057833

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Contents

Kamel Abdelouahab, Maxime Pelcat, and Franois Berry

Kaidong Li, Wenchi Ma, Usman Sajid, Yuanwei Wu, and Guanghui Wang

Khan Muhammad, Salman Khan, and Sung Wook Baik

Alaa S. Al-Waisy, Shumoos Al-Fahdawi, and Rami Qahwaji

Amin Ullah, Khan Muhammad, Tanveer Hussain, Miyoung Lee, and Sung Wook Baik

Hazem Rashed, Senthil Yogamani, Ahmad El-Sallab, Mahmoud Hassaballah, and Mohamed ElHelw

Ahmed Nassar, and Mohamed ElHelw

Javier Ruiz-del-Solar and Patricio Loncomilla

Mahmoud Khaled Abd-Ellah, Ali Ismail Awad, Ashraf A. M. Khalaf, and Hesham F. A. Hamed

Mohammed A. Al-masni, Mugahed A. Al-antari, and Tae-Seong Kim

Khalid M. Hosny, Mohamed A. Kassem, and Mohamed M. Foaud

Deep learning, while it has multiple definitions in the literature, can be defined as inference of model parameters for decision making in a process mimicking the understanding process in the human brain; or, in short: brain-like model identification. We can say that deep learning is a way of data inference in machine learning, and the two together are among the main tools of modern artificial intelligence. Novel technologies away from traditional academic research have fueled R&D in convolutional neural networks (CNNs); companies like Google, Microsoft, and Facebook ignited the art of data manipulation, and the term deep learning became almost synonymous with decision making.

Various CNN structures have been introduced and invoked in many computer vision-related applications, with greatest success in face recognition, autonomous driving, and text processing. The reality is: deep learning is an art, not a science. This state of affairs will remain until its developers develop the theory behind its functionality, which would lead to cracking its code and explaining why it works, and how it can be structured as a function of the information gained with data. In fact, with deep learning, there is good and bad news. The good news is that the industrynot necessarily academiahas adopted it and is pushing its envelope. The bad news is that the industry does not share its secrets. Indeed, industries are never interested in procedural and textbook-style descriptions of knowledge.

This book, Deep Learning in Computer Vision: Principles and Applications as a journey in the progress made through deep learning by academiaconfines itself to deep learning for computer vision, a domain that studies sensory information used by computers for decision making, and has had its impacts and drawbacks for nearly 60 years. Computer vision has been and continues to be a system: sensors, computer, analysis, decision making, and action. This system takes various forms and the flow of information within its components, not necessarily in tandem. The linkages between computer vision and machine learning, and between it and artificial intelligence, are very fuzzy, as is the linkage between computer vision and deep learning. Computer vision has moved forward, showing amazing progress in its short history. During the sixties and seventies, computer vision dealt mainly with capturing and interpreting optical data. In the eighties and nineties, geometric computer vision added science (geometry plus algorithms) to computer vision. During the first decade of the new millennium, modern computing contributed to the evolution of object modeling using multimodality and multiple imaging. By the end of that decade, a lot of data became available, and so the term deep learning crept into computer vision, as it did into machine learning, artificial intelligence, and other domains.