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.
Machine Learning for OpenCV
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Machine Learning for OpenCV
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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)
Preface
Free Chapter
A Taste of Machine Learning
Working with Data in OpenCV and Python
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
Combining Different Algorithms into an Ensemble
Selecting the Right Model with Hyperparameter Tuning
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