Machine Learning for OpenCV 4

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.

Type
ebook
Category
Data
publication date
2019-09-06
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

no of pages
420
duration
840
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
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 4 and its powerful machine learning classes while demonstrating how to get to grips with a range of models.
audience
This 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.
meta 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
short description
Machine Learning for OpenCV 4, Second Edition will help the readers to implement and train machine learning algorithms with OpenCV 4 and scikit-learn in Python. By the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your machine learning algorithms.
subtitle
Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn
keywords
machine learning, opencv, computer vision, algorithms
Product ISBN
9781789536300