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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
Languages
Tools
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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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Toc

Table of Contents (15) Chapters Close

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

Recovering camera pose

When a camera is calibrated, it becomes possible to relate the captured images with the outside world. We previously explained that if the 3D structure of an object is known, then one can predict how the object will be imaged on the sensor of the camera. The process of image formation is indeed completely described by the projective equation that was presented at the beginning of this chapter. When most of the terms of this equation are known, then it becomes possible to infer the value of the other elements (2D or 3D) through the observation of some images. In this recipe, we will look at the camera pose recovery problem when a known 3D structure is observed.

How to do it...

Let's consider a simple object, a bench in a park. We took an image of this one using the camera/lens system calibrated in the previous recipe. We also have manually identified eight distinct image points on the bench that we will use for our camera pose estimation:

How to do it...

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