Book Image

Machine Learning for OpenCV 4 - Second Edition

By : Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler
Book Image

Machine Learning for OpenCV 4 - Second Edition

By: Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler

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)
Free Chapter
1
Section 1: Fundamentals of Machine Learning and OpenCV
6
Section 2: Operations with OpenCV
11
Section 3: Advanced Machine Learning with OpenCV

Approaching a machine learning problem

When you see a new machine learning problem in the wild, you might be tempted to jump ahead and throw your favorite algorithm at the problem—perhaps the one you understood best or had the most fun implementing. But knowing beforehand which algorithm will perform best on your specific problem is not often possible.

Instead, you need to take a step back and look at the bigger picture. Before you get in too deep, you will want to define the actual problem you are trying to solve. For example, do you already have a specific goal in mind, or are you just looking to do some exploratory analysis and find something interesting in the data? Often, you will start with a general goal, such as detecting spam email messages, making movie recommendations, or automatically tagging your friends in pictures uploaded to a social media platform. However...