Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

By : Joseph Howse, Joe Minichino
5 (2)
Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

5 (2)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases 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 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, 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. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
Table of Contents (12 chapters)
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Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
Appendix A: Bending Color Space with the Curves Filter

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 by analyzing circular neighborhoods of 16 pixels. It marks each pixel in a neighborhood as brighter or darker than a particular threshold, which is defined relative to the center of the circle. A neighborhood is deemed to be a corner if it contains a number of contiguous...