Machine Learning for OpenCV 4 - Second Edition

4 (2 reviews total)
By Aditya Sharma , Vishwesh Ravi Shrimali , Michael Beyeler
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  1. Section 1: Fundamentals of Machine Learning and OpenCV

About 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.

Publication date:
September 2019
Publisher
Packt
Pages
420
ISBN
9781789536300

 

Section 1: Fundamentals of Machine Learning and OpenCV

In the very first section of this book, we will go over the basics of machine learning and OpenCV, starting with installing the required libraries, and then moving on to basic OpenCV functions, the basics of supervised learning and their applications, and finally, feature detection and recognition using OpenCV.

This section includes the following chapters:

  • Chapter 1A Taste of Machine Learning
  • Chapter 2Working with Data in OpenCV
  • Chapter 3First Steps in Supervised Learning
  • Chapter 4Representing Data and Engineering Features

About the Authors

  • Aditya Sharma

    Aditya Sharma is a senior engineer at Robert Bosch working on solving real-world autonomous computer vision problems. At Robert Bosch, he also secured first place at an AI hackathon 2019. He has been associated with some of the premier institutes of India, including IIT Mandi and IIIT Hyderabad. At IIT, he published papers on medical imaging using deep learning at ICIP 2019 and MICCAI 2019. At IIIT, his work revolved around document image super-resolution. He is a motivated writer and has written many articles on machine learning and deep learning for DataCamp and LearnOpenCV. Aditya runs his own YouTube channel and has contributed as a speaker at the NCVPRIPG conference (2017) and Aligarh Muslim University for a workshop on deep learning.

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  • Vishwesh Ravi Shrimali

    Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering. Since then he has been working with BigVision LLC in deep learning and computer vision and is also involved in creating official OpenCV courses. He has a keen interest in programming and AI and has applied that interest in mechanical engineering projects. He has also written multiple blogs on OpenCV, deep learning on LearnOpenCV, and on computer vision. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.

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  • Michael Beyeler

    Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye). His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.

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Latest Reviews

(2 reviews total)
Buono ma con contenuti ripetuti da altri libri della stessa Packt Queste duplicazioni danno fastidio
Pretty good book. Has all the requirements needed to adhere with ML.

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