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Full Description
A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4
Key Features
Gain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learn
Get up to speed with Intel OpenVINO and its integration with OpenCV 4
Implement high-performance machine learning models with helpful tips and best practices
Book DescriptionOpenCV 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.
What you will learn
Understand the core machine learning concepts for image processing
Explore the theory behind machine learning and deep learning algorithm design
Discover effective techniques to train your deep learning models
Evaluate machine learning models to improve the performance of your models
Integrate algorithms such as support vector machines and Bayes classifier in your computer vision applications
Use OpenVINO with OpenCV 4 to speed up model inference
Who this book is forThis book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.
Contents
Table of Contents
A Taste of Machine Learning
Working with Data in OpenCV
First Steps in Supervised Learning
Representing Data and Engineering Features
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
Using Deep Learning to Classify Handwritten Digits
Ensemble Methods for Classification
Selecting the Right Model with Hyperparameter Tuning
Using OpenVINO with OpenCV
Conclusion
