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

Face detection and initialization


The method for face tracking described thus far has assumed that the facial features in the image are located within a reasonable proximity to the current estimate. Although this assumption is reasonable during tracking, where face motion between frames is often quite small, we are still faced with the dilemma of how to initialize the model in the first frame of the sequence. An obvious choice for this is to use OpenCV's in-built cascade detector to find the face. However, the placement of the model within the detected bounding box will depend on the selection made for the facial features to track. In keeping with the data-driven paradigm we have followed so far in this chapter, a simple solution is to learn the geometrical relationship between the face detection's bounding box and the facial features.

The face_detector class implements exactly this solution. A snippet of its declaration that highlights its functionality is given as follows:

class face_detector...