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

Marker-based versus marker-less AR


From the previous chapter you've learned how to use special images called markers to augment a real scene. The strong aspects of the markers are as follows:

  • Cheap detection algorithm

  • Robust against lighting changes

Markers also have several weaknesses. They are as follows:

  • Doesn't work if partially overlapped

  • Marker image has to be black and white

  • Has square form in most cases (because it's easy to detect)

  • Non-esthetic visual look of the marker

  • Has nothing in common with real-world objects

So, markers are a good point to start working with augmented reality; but if you want more, it's time to move on from marker-based to marker-less AR. Marker-less AR is a technique that is based on recognition of objects that exist in the real world. A few examples of a target for marker-less AR are: magazine covers, company logos, toys, and so on. In general, any object that has enough descriptive and discriminative information regarding the rest of the scene can be a target...