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

Active Shape Models


As mentioned previously, AAMs require a shape model, and this role is played by Active Shape Models (ASMs). In the coming sections, we will create an ASM that is a statistical model of shape variation. The shape model is generated through the combination of shape variations. A training set of labeled images is required, as described in the article Active Shape Models – Their Training and Application, by Timothy Cootes. In order to build a face-shape model, several images marked with points on key positions of a face are required to outline the main features. The following screenshot shows such an example:

There are 76 landmarks on a face, which are taken from the MUCT dataset. These landmarks are usually marked up by hand and they outline several face features, such as mouth contour, nose, eyes, eyebrows, and face shape, since they are easier to track.

Note

Procrustes Analysis: A form of statistical shape analysis used to analyze the distribution of a set of shapes. Procrustes...