Book Image

Mastering OpenCV with Practical Computer Vision Projects

Book Image

Mastering OpenCV with Practical Computer Vision Projects

Overview of this book

Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials.Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV's new C++ interface before migrating from the C API to the C++ API.Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you're most interested in.Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on. Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects.
Table of Contents (15 chapters)
Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 3. Marker-less Augmented Reality

In this chapter readers will learn how to create a standard real-time project using OpenCV (for desktop), and how to perform a new method of marker-less augmented reality, using the actual environment as the input instead of printed square markers. This chapter will cover some of the theory of marker-less AR and show how to apply it in useful projects.

The following is a list of topics that will be covered in this chapter:

  • Marker-based versus marker-less AR

  • Using feature descriptors to find an arbitrary image on video

  • Pattern pose estimation

  • Application infrastructure

  • Enabling support for OpenGL visualization in OpenCV

  • Rendering the augmented reality

  • Demonstration

Before we start, let me give you a brief list of the knowledge required for this chapter and the software you will need:

  • Basic knowledge of CMake. CMake is a cross-platform, open-source build system designed to build, test, and package software. Like the OpenCV library, the demonstration project for...