Book Image

Machine Learning for OpenCV

By : Michael Beyeler, Michael Beyeler (USD)
Book Image

Machine Learning for OpenCV

By: Michael Beyeler, Michael Beyeler (USD)

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)

Understanding the McCulloch-Pitts neuron

In 1943, Warren McCulloch and Walter Pitts published a mathematical description of neurons as they were believed to operate in the brain. A neuron receives input from other neurons through connections on its dendritic tree, which are integrated to produce an output at the cell body (or soma). The output is then communicated to other neurons via a long wire (or axon), which eventually branches out to make one or more connections (at axon terminals) on the dendritic tree of other neurons. An example neuron is shown in the following figure:

Schematic of a neuron (nerve cell). Adapted from a figure by Looxix at French Wikipedia (CC BY-SA 3.0)

McCulloch and Pitts described the inner workings of such a neuron as a simple logic gate that would be either on or off, depending on the input it receives on its dendritic tree. Specifically, the neuron...