Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino
Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)

Using ORB with FAST features and BRIEF descriptors

If SIFT is young, and SURF younger, ORB is in its infancy. ORB was first published in 2011 as a fast alternative to SIFT and SURF.

The algorithm was published in the paper ORB: an efficient alternative to SIFT or SURF, available in PDF format at

ORB mixes the techniques used in the FAST keypoint detector and the BRIEF keypoint descriptor, so it is worth taking a quick look at FAST and BRIEF first. Then, we will talk about brute-force matching an algorithm used for feature matching and look at an example of feature matching.


The Features from Accelerated Segment Test (FAST) algorithm works...