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

Reconstructing the scene


Next we look into the matter of recovering the 3D structure of the scene from the information we have acquired so far. As we had done before, we should look at the tools and information we have at hand to achieve this. In the preceding section we obtained two camera matrices from the essential and fundamental matrices; we already discussed how these tools will be useful for obtaining the 3D position of a point in space. Then, we can go back to our matched point pairs to fill in our equations with numerical data. The point pairs will also be useful in calculating the error we get from all our approximate calculations.

This is the time to see how we can perform triangulation using OpenCV. This time we will follow the steps Hartley and Sturm take in their article Triangulation, where they implement and compare a few triangulation methods. We will implement one of their linear methods, as it is very simple to code with OpenCV.

Remember we had two key equations arising...