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

Introduction to OpenVINO

OpenVINO (short for Open Visual Inferencing and Neural Network Optimization). It is designed to optimize various neural networks to speed up the inference stage. Inference, as we have discussed in previous chapters, is the process in which a trained neural network is used to generate results with unseen input data. For example, if a network is trained to classify a dog or cat, then if we feed the image of Tuffy (our neighbor's dog), it should be able to infer that the image is of a dog.

Considering that images and videos have become so common in today's world, there are a lot of deep neural networks trained to perform various operations, such as, multilabel classification, and motion tracking. Most of the inference performed in the world occurs on CPUs since GPUs are very expensive and usually do not suit the budget of individual AI engineers...