As the world changes and humans build smarter and better machines, the demand for machine learning and computer vision experts increases. Machine learning, as the name suggests, is the process of a machine learning to make predictions given a certain set of parameters as input. Computer vision, on the other hand, gives a machine vision; that is, it makes the machine aware of visual information. When you combine these technologies, you get a machine that can use visual data to make predictions, which brings machines one step closer to having human capabilities. When you add deep learning to it, the machine can even surpass human capabilities in terms of making predictions. This might seem far-fetched, but with AI systems taking over decision-based systems, this has actually become a reality. You have AI cameras, AI monitors, AI sound systems, AI-powered processors, and more. We cannot promise you that you will be able to build an AI camera after reading this book, but we do intend to provide you with the tools necessary for you to do so. The most powerful tool that we are going to introduce is the OpenCV library, which is the world's largest computer vision library. Even though its use in machine learning is not very common, we have provided some examples and concepts on how it can be used for machine learning. We have gone with a hands-on approach in this book and we recommend that you try out every single piece of code present in this book to build an application that showcases your knowledge. The world is changing and this book is our way of helping young minds change it for the better.
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Table Of Contents
Machine Learning for OpenCV 4 - Second Edition
By :
Machine Learning for OpenCV 4
By:
Overview of this book
OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.
You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.
By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.
Table of Contents (18 chapters)
Preface
Free Chapter
Section 1: Fundamentals of Machine Learning and OpenCV
A Taste of Machine Learning
Working with Data in OpenCV
First Steps in Supervised Learning
Representing Data and Engineering Features
Section 2: Operations with OpenCV
Using Decision Trees to Make a Medical Diagnosis
Detecting Pedestrians with Support Vector Machines
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
Section 3: Advanced Machine Learning with OpenCV
Using Deep Learning to Classify Handwritten Digits
Ensemble Methods for Classification
Selecting the Right Model with Hyperparameter Tuning
Using OpenVINO with OpenCV
Conclusion
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