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)
Free Chapter
1
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

Understanding types of feature detection and matching

A number of algorithms can be used to detect and describe features, and we will explore several of them in this section. The most commonly used feature detection and descriptor extraction algorithms in OpenCV are as follows:

  • Harris: This algorithm is useful for detecting corners.
  • SIFT: This algorithm is useful for detecting blobs.
  • SURF: This algorithm is useful for detecting blobs.
  • FAST: This algorithm is useful for detecting corners.
  • BRIEF: This algorithm is useful for detecting blobs.
  • ORB: This algorithm stands for Oriented FAST and Rotated BRIEF. It is useful for detecting a combination of corners and blobs.

Matching features can be performed with the following methods:

  • Brute-force matching
  • FLANN-based matching

Spatial verification can then be performed with homography.

We have just introduced a lot of new terminology and algorithms. Now, we will go over their basic definitions.

Defining features

What is a feature, exactly...