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Michael Beyeler - Machine Learning for OpenCV: Intelligent Image Processing with Python

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Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book - Load, store, edit, and visualize data using OpenCV and Python - Grasp the fundamental concepts of classification, regression, and clustering - Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide - Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn - Explore and make effective use of OpenCVs machine learning module - Learn deep learning for computer vision with Python - Master linear regression and regularization techniques - Classify objects such as flower species, handwritten digits, and pedestrians - Explore the effective use of support vector machines, boosted decision trees, and random forests - Get acquainted with neural networks and Deep Learning to address real-world problems - Discover hidden structures in your data using k-means clustering - Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of todays most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Googles DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on todays hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch! Style and approach OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.

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Machine Learning for OpenCV Intelligent image processing with Python Michael - photo 1
Machine Learning for OpenCV
Intelligent image processing with Python
Michael Beyeler
BIRMINGHAM - MUMBAI Machine Learning for OpenCV Copyright 2017 Packt Publishing - photo 2

BIRMINGHAM - MUMBAI

Machine Learning for OpenCV

Copyright 2017 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: July 2017

Production reference: 1130717

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.

ISBN 978-1-78398-028-4

www.packtpub.com

Credits Author Michael Beyeler Copy Editor Manisha Sinha - photo 3

Credits

Author

Michael Beyeler

Copy Editor

Manisha Sinha

Reviewers

Vipul Sharma
Rahul Kavi

Project Coordinator

Manthan Patel

Commissioning Editor

Veena Pagare

Proofreader

Safis Editing

Acquisition Editor

Varsha Shetty

Indexer

Tejal Daruwale Soni

ContentDevelopmentEditor

Jagruti Babaria

Graphics

Tania Dutta

Technical Editor

Sagar Sawant

Production Coordinator

Deepika Naik

Foreword

Over the last few years, our machines have slowly but surely learned how to see for themselves. We now take it for granted that our cameras detect our faces in pictures that we take, and that social media apps can even recognize us and our friends in the photos that we upload from these cameras. Over the next few years we will experience even more radical transformation. Before long, cars will be driving themselves, our cellphones will be able to read and translate a sign in any language for us, and our x-rays and other medical images will be read and analyzed by powerful algorithms that will be able to accurately suggest a medical diagnosis, and even recommend effective treatments.

These transformations are driven by an explosive combination of increased computing power, masses of image data, and a set of clever ideas taken from math, statistics, and computer science. This rapidly growing intersection that is machine learning has taken off, affecting many of our day-to-day interactions with the world, and with each other. One of the most remarkable features of the current machine learning paradigm-shift in computer vision is that it relies to a large extent on software tools that are freely available and developed by large groups of volunteers, hobbyists, scientists, and engineers in open source communities. This means that, in principle, the barriers to entry are also lower than ever: anyone who is interested in putting their mind to it can harness machine learning for image processing.

However, just like in a garden with many forking paths, the wealth of tools and ideas, and the rapid development of these ideas, underscores the need for a guide who can show you the way, and orient you in the right direction. I have some good news for you: having picked up this book, you are in the good hands of my colleague and collaborator Dr. Michael Beyeler as your guide. With his broad range of expertise, Michael is both a hard-nosed engineer, computer scientist, and neuroscientist, as well as a prolific open source software developer. He has not only taught robots how to see and navigate through complex environments, and computers how to model brain activity, but he also regularly teaches humans how to use programming to solve a variety of different machine learning and image processing problems. This means that you will get to benefit not only from the sure-handed rigor of his expertise and experience, but also that you will get to enjoy his thoughtfulness in teaching the ideas in his book, as well as a good dose of his sense of humor.

The second piece of good news is that this going to be an exhilarating trip. There's nothing that matches the thrill of understanding that comes from putting together the pieces of the puzzle that go into solving a problem in computer vision and machine learning with code and data. As Richard Feynman put it: "What I cannot create, I do not understand". So, get ready to get your hands dirty (so to speak) with the code and data in the (open source!) code examples that accompany this book, and to get creative. Understanding will surely follow.

Ariel Rokem
Data Scientist, The University of Washington eScience Institute
About the Author

Michael Beyeler is a Postdoctoral Fellow in Neuroengineering and Data Science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye). His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. Michael is the author of OpenCV with Python Blueprints by Packt Publishing, 2015, a practical guide for building advanced computer vision projects. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android.
Michael received a PhD in computer science from the University of California, Irvine as well as a MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland. When he is not "nerding out" on brains, he can be found on top of a snowy mountain, in front of a live band, or behind the piano.

About the Reviewers

Vipul Sharma is a Software Engineer at a startup in Bangalore, India. He studied engineering in Information Technology at Jabalpur Engineering College (2016). He is an ardent Python fan and loves building projects on computer vision in his spare time. He is an open source enthusiast and hunts for interesting projects to contribute to. He is passionate about learning and strives to better himself as a developer. He writes blogs on his side projects at http://vipul.xyz. He also publishes his code at http://github.com/vipul-sharma20 .

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