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

Estimating the camera motion from a pair of images


Before we set out to actually find the motion between two cameras, let us examine the inputs and the tools we have at hand to perform this operation. First, we have two images of the same scene from (hopefully not extremely) different positions in space. This is a powerful asset, and we will make sure to use it. Now as far as tools go, we should take a look at mathematical objects that impose constraints over our images, cameras, and the scene.

Two very useful mathematical objects are the fundamental matrix (denoted by F) and the essential matrix (denoted by E). They are mostly similar, except that the essential matrix is assuming usage of calibrated cameras; this is the case for us, so we will choose it. OpenCV only allows us to find the fundamental matrix via the findFundamentalMat function; however, it is extremely simple to get the essential matrix from it using the calibration matrix K as follows:

Mat_<double> E = K.t() * F * K...