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

Common pitfalls and suggested solutions


OpenCV is very feature rich and provides multiple solutions and paths to resolve a visual-understanding problem. With this great power also comes hard work, choosing and crafting the best processing pipeline for the project requirements. Having multiple options means that probably finding the exact best performing solution is next to impossible, as many pieces are interchangeable and testing all the possible options is out of our reach. This problem's exponential complexity is compounded by the input data; more unknown variance in the incoming data will make our algorithm choices even more unstable. In other words, working with OpenCV, or any other computer vision library, is still a matter of experience and art. A priori intuition as to the success of one or another route to a solution is something computer vision engineers develop with years of experience, and for the most part there are no shortcuts.

There is, however, the option of learning from...