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Machine Learning for OpenCV 4

Machine Learning for OpenCV 4

By : Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali , Michael Beyeler
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Machine Learning for OpenCV 4

Machine Learning for OpenCV 4

By: Sharma, Michael Beyeler (USD), 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)
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Section 1: Fundamentals of Machine Learning and OpenCV
6
Section 2: Operations with OpenCV
11
Section 3: Advanced Machine Learning with OpenCV

Using Deep Learning to Classify Handwritten Digits

Let's now return to supervised learning and discuss a family of algorithms known as artificial neural networks. Early studies of neural networks go back to the 1940s when Warren McCulloch and Walter Pitts first described how biological nerve cells (or neurons) in the brain might work. More recently, artificial neural networks have seen a revival under the buzzword deep learning, which powers state-of-the-art technologies such as Google's DeepMind and Facebook's DeepFace algorithms.

In this chapter, we want to wrap our heads around some simple versions of artificial neural networks, such as the McCulloch-Pitts neuron, the perceptron, and the multilayer perceptron. Once we have familiarized ourselves with the basics, we will be ready to implement a more sophisticated deep neural network to classify handwritten digits...

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