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

Summary


In this chapter, we have discussed how active appearance models can be combined with the POSIT algorithm in order to obtain a 3D head pose. An overview on how to create, train, and manipulate AAMs has been given and the reader can use this background for any other field, such as medical, imaging, or industry. Besides dealing with AAMs, we got familiar to Delaunay subdivisions and learned how to use such an interesting structure as a triangulated mesh. We also showed how to perform texture mapping in the triangles using OpenCV functions. Another interesting topic was approached in AAM fitting. Although only the inverse compositional project-out algorithm was described, we could easily obtain the results of years of research by simply using its output.

After enough theory and practice of AAMs, we dived into the details of POSIT in order to couple 2D measurements to 3D ones explaining how to fit a 3D model using matchings between model points. We concluded the chapter by showing how...