Sandipan Dey - Image Processing Masterclass with Python: 50+ Solutions and Techniques Solving Complex Digital Image Processing Challenges Using Numpy, Scipy, Pytorch and Keras (English Edition)

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Image Processing

Masterclass with

Python

50+ Solutions and Techniques Solving

Complex Digital Image Processing

Challenges Using Numpy, Scipy,

Pytorch and Keras

Sandipan Dey

www.bpbonline.com

FIRST EDITION 2021

Copyright BPB Publications, India

ISBN: 978-93-89898-64-4

All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means.

LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY

The information contained in this book is true to correct and the best of authors and publishers knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book.

All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information.

Distributors:

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Published by Manish Jain for BPB Publications, Ansari Road, Darya Ganj, New Delhi-110002 and Printed by him at Repro India Ltd, Mumbai

www.bpbonline.com

Dedicated to

My beloved Parents,

Shri Sanjit Kumar Dey

Shrimati Sila Dey

&

My Uncles and Aunts

About the Author

Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, such as recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his masters degree in computer science from the University of Maryland, Baltimore County and has published in a few IEEE data mining conferences and journals. He has also authored a couple of image processing books, published from an international publication house. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, natural language processing, artificial intelligence, algorithms, statistics, mathematics, and related courses. He is a regular blogger data science central, and medium.com) and is a machine learning education enthusiast.

About the Reviewer

Jyoti Dabass is a Full-Time Ph.D. Scholar at The Northcap University, Gurugram. Her research interest is focused on computer vision, deep learning, fuzzy logic, machine learning, artificial intelligence, image processing, computer-aided diagnosis, and natural language processing. She has earned her masters degree from J.C. Bose University of Science and Technology, YMCA, Faridabad and has published more than papers in International Journals and conferences. She has earned certifications from 400+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. She believes this book will give you enough hands-on experience to jump-start your career in computer vision.

Acknowledgements

There are a few people who I want to thank for the continued and ongoing support they have given me during the writing of this book. First and foremost, I would like to thank my parents for continuously encouraging me to write the bookI could have never completed this book without their support.

I am grateful to the excellent online courses provided by top schools across the globe over the past few years. Few of them are image processing (@Coursera by Duke, Northwestern), computer vision and image analysis (@edX by Microsoft), computational photography (@Coursera by Georgia Tech), machine learning (@Coursera by Stanford, University of Toronto; @edX by UCSD), and deep learning (@Coursera by deeplearning.ai; @Udacity by Google).

My gratitude also goes to the team at BPB Publication (including Surbhi and Anugraha) for being supportive enough to provide me quite a long time to finish the first part of the book and also to allow me to publish the book in multiple parts, as image processing is a vast and an active area of research, it was impossible to deep-dive into a different class of problems in a single book, especially by not making it too voluminous.

Preface

This book covers many different aspects of image processing through different types of problems starting from standard image processing to the recent advancements in image processing with machine learning and deep learning models. The key focus is on the implementation of different algorithms using python libraries to solve an exhaustive set of important and relevant image processing problems (with different levels of complexity) in more than one way.

The book describes the theoretical concepts required to solve a problem, discussed on the mathematical perspective as well as on the intuitive one, to make it easy for the readers to understand. It demonstrates the impact of running an algorithm on a set of colored images with input/output images. It also provides some cutting-edge solutions to a few advanced problems from the leading image processing research conferences/journals. Every chapter is followed by quite a few challenging exercise problems to create an opportunity for the readers to test the skills they have learned and got hands-on.

The book takes a problem-oriented approachit focusses on solving many different types of problems (of different levels of difficulty) from the vast majority of areas in digital image processing, by explaining the underlying theory, as well as providing hands-on implementations. Considering the trade-off between the vast majority of topics to be covered in the book and to keep the volume manageable, this book will be released in two parts.

Part-1 will start from the basics and will gradually proceed towards solving advanced problems. It will start with problems based on basic image processing and manipulation techniques. Then, it will focus on classical image processing algorithms that come from signal processing, for example, sampling, convolution, Fourier transform, frequency domain filtering, and more. Then, it will proceed further to solve problems on image enhancement, for example, using spatial domain filtering. It will also discuss a few machine learning and deep learning model-based approaches to solve quite a few popular image processing problems. Last but not the least, it will also have some sections on solving facial image processing problems, such as face detection, recognition, and more; these problems are gaining more and more interest from image processing communities nowadays.

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