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OpenCV 3 Computer Vision Application Programming Cookbook

You're reading from   OpenCV 3 Computer Vision Application Programming Cookbook Recipes to make your applications see

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Product type Paperback
Published in Feb 2017
Publisher
ISBN-13 9781786469717
Length 474 pages
Edition 3rd Edition
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Author (1):
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Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
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Table of Contents (21) Chapters Close

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition
Credits
About the Author
About the Reviewer
www.PacktPub.com
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Preface
1. Playing with Images FREE CHAPTER 2. Manipulating Pixels 3. Processing the Colors of an Image 4. Counting the Pixels with Histograms 5. Transforming Images with Morphological Operations 6. Filtering the Images 7. Extracting Lines, Contours, and Components 8. Detecting Interest Points 9. Describing and Matching Interest Points 10. Estimating Projective Relations in Images 11. Reconstructing 3D Scenes 12. Processing Video Sequences 13. Tracking Visual Motion 14. Learning from Examples

Computing the fundamental matrix of an image pair


The introductory section of this chapter presented the projective equation, describing how a scene point projects onto the image plane of a single camera. In this recipe, we will explore the projective relationship that exists between two images that display the same scene. These two images could have been obtained by moving a camera to two different locations to take pictures from two viewpoints, or by using two cameras, each of them taking a different picture of the scene. When those two cameras are separated by a rigid baseline, we use the term stereovision.

Getting ready

Let's now consider two pinhole cameras observing a given scene point, as shown in the following figure:

We learned that we can find the image x of a 3D point X by tracing a line joining this 3D point with the camera's center. Conversely, the scene point that has its image at position x on the image plane can be located anywhere on this line in the 3D space. This implies...

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