Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 20. Finding the Best OpenCV Algorithm for the Job

Any computer vision problem can be solved in different ways. Each way has its pros and cons and relative measures of success, depending on the data, resources, or goals. Working with OpenCV, a computer vision engineer has many algorithmic options on hand to solve a given task. Making the right choice in an informed way is extremely important since it can have a tremendous impact on the success of the entire solution, and prevent you from being boxed into a rigid implementation. This chapter will discuss some methods to follow when considering options in OpenCV. We will discuss the areas in computer vision that OpenCV covers, ways to select between competing algorithms if more than one exists, how to measure the success of an algorithm, and finally how to measure success in a robust way with a pipeline.

The following topics will be covered in this chapter:

  • Is it covered in OpenCV? Computer vision topics with algorithms available in OpenCV...