Book Image

Mastering OpenCV with Practical Computer Vision Projects

By : Mora Saragih, Eugene Khvedchenia, Daniel L√É∆í¬©lis Baggio, Shervin Emami, Khvedchenia Ievgen, Jason Saragih, Daniel Lelis Baggio, OpenCV Project, David Millán Escrivá, Roy Shilkrot, Naureen Mahmood
Book Image

Mastering OpenCV with Practical Computer Vision Projects

By: Mora Saragih, Eugene Khvedchenia, Daniel L√É∆í¬©lis Baggio, Shervin Emami, Khvedchenia Ievgen, Jason Saragih, Daniel Lelis Baggio, OpenCV Project, David Millán Escrivá, Roy Shilkrot, Naureen Mahmood

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

Overview


Non-rigid face tracking was first popularized in the early to mid 90s with the advent of active shape models (ASM) by Cootes and Taylor. Since then, a tremendous amount of research has been dedicated to solving the difficult problem of generic face tracking with many improvements over the original method that ASM proposed. The first milestone was the extension of ASM to active appearance models (AAM) in 2001, also by Cootes and Taylor. This approach was later formalized though the principled treatment of image warps by Baker and colleges in the the mid 2000s. Another strand of work along these lines was the 3D Morphable Model (3DMM) by Blanz and Vetter, which like AAM, not only modeled image textures as opposed to profiles along object boundaries as in ASM, but took it one step further by representing the models with a highly dense 3D data learned from laser scans of faces. From the mid to the late 2000s, the focus of research on face tracking shifted away from how the face was...