Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

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 today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s 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 today's 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!
Table of Contents (13 chapters)

Representing images

One of the most common and important data types for computer vision are, of course, images. The most straightforward way to represent images is probably by using the grayscale value of each pixel in the image. Usually, grayscale values are not very indicative of the data they describe. For example, if we saw a single pixel with grayscale value 128, could we tell what object this pixel belonged to? Probably not. Therefore, grayscale values are not very effective image features.

Using color spaces

Alternatively, we might find that colors contain some information that raw grayscale values cannot capture. Most often, images come in the conventional RGB color space, where every pixel in the image gets an intensity...