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

Summary


In this chapter we have seen how OpenCV can help us approach Structure from Motion in a manner that is both simple to code and to understand. OpenCV's API contains a number of useful functions and data structures that make our lives easier and also assist in a cleaner implementation.

However, the state-of-the-art SfM methods are far more complex. There are many issues we choose to disregard in favor of simplicity, and plenty more error examinations that are usually in place. Our chosen methods for the different elements of SfM can also be revisited. For one, H and Z propose a highly accurate triangulation method that minimizes the reprojection error in the image domain. Some methods even use the N-view triangulation once they understand the relationship between the features in multiple images.

If we would like to extend and deepen our familiarity with SfM, we will certainly benefit from looking at other open-source SfM libraries. One particularly interesting project is libMV, which...