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

Chapter 4. Exploring Structure from Motion Using OpenCV

In this chapter we will discuss the notion of Structure from Motion (SfM), or better put as extracting geometric structures from images taken through a camera's motion, using functions within OpenCV's API to help us. First, let us constrain the otherwise lengthy footpath of our approach to using a single camera, usually called a monocular approach, and a discrete and sparse set of frames rather than a continuous video stream. These two constrains will greatly simplify the system we will sketch in the coming pages, and help us understand the fundamentals of any SfM method. To implement our method we will follow in the footsteps of Hartley and Zisserman (hereafter referred to as H and Z), as documented in chapters 9 through 12 of their seminal book Multiple View Geometry in Computer Vision.

In this chapter we cover the following:

  • Structure from Motion concepts

  • Estimating the camera motion from a pair of images

  • Reconstructing the scene

  • Reconstruction...