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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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1
Section 1: Fundamentals of Machine Learning and OpenCV
6
Section 2: Operations with OpenCV
11
Section 3: Advanced Machine Learning with OpenCV

Conclusion

Congratulations! You have just made a big step toward becoming a machine learning practitioner. Not only are you familiar with a wide variety of fundamental machine learning algorithms, but you also know how to apply them to both supervised and unsupervised learning problems. Moreover, you were introduced to a new and exciting topic, OpenVINO Toolkit. In the previous chapter, we learned how to install OpenVINO and run an interactive face detection and image classification demo, among others. I am sure you have enjoyed learning about those topics.

Before we part ways, I want to give you some final words of advice, point you toward some additional resources, and give you some suggestions on how you can further improve your machine learning and data science skills. In this chapter, we will learn how to approach a machine learning problem and build our own estimator. We...

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