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  • Book Overview & Buying Mastering OpenCV with Practical Computer Vision Projects
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Mastering OpenCV with Practical Computer Vision Projects

Mastering OpenCV with Practical Computer Vision Projects

4.2 (17)
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Mastering OpenCV with Practical Computer Vision Projects

Mastering OpenCV with Practical Computer Vision Projects

4.2 (17)

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)
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Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1
Index

POSIT


After we have found the 2D position of our landmark points, we can derive the 3D pose of our model using the POSIT. The pose P of a 3D object is defined as the 3 x 3 rotation matrix R and the 3D translation vector T, hence P is equal to [ R | T ].

Note

Most of this section is based on the OpenCV POSIT tutorial by Javier Barandiaran.

As the name implies, POSIT uses the Pose from Orthography and Scaling (POS) algorithm in several iterations, so it is an acronym for POS with Iterations. The hypothesis for its working is that we can detect and match in the image four or more non-coplanar feature points of the object and that we know their relative geometry on the object.

The main idea of the algorithm is that we can find a good approximation to the object pose, supposing that all the model points are in the same plane, since their depths are not very different from one another if compared to the distance from the camera to a face. After the initial pose is obtained, the rotation matrix and...

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