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Learning OpenCV 4 Computer Vision with Python 3

You're reading from   Learning OpenCV 4 Computer Vision with Python 3 Get to grips with tools, techniques, and algorithms for computer vision and machine learning

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781789531619
Length 372 pages
Edition 3rd Edition
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Authors (2):
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Joe Minichino Joe Minichino
Author Profile Icon Joe Minichino
Joe Minichino
Joseph Howse Joseph Howse
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Joseph Howse
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Table of Contents (13) Chapters Close

Preface 1. Setting Up OpenCV 2. Handling Files, Cameras, and GUIs FREE CHAPTER 3. Processing Images with OpenCV 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. Other Book You May Enjoy Appendix A: Bending Color Space with the Curves Filter

Understanding 3D image tracking and augmented reality

We have already solved problems involving image matching in Chapter 6, Retrieving Images and Searching Using Image Descriptors. Moreover, we have solved problems involving continuous tracking in Chapter 8, Tracking Objects. Therefore, we are familiar with many of the components of an image tracking system, though we have not yet tackled any 3D tracking problems.

So, what exactly is 3D tracking? Well, it is the process of continually updating an estimate of an object's pose in a 3D space, typically, in terms of six variables: three variables to represent the object's 3D translation (that is, position) and the other three variables to represent its 3D rotation.

A more technical term for 3D tracking is 6DOF tracking – that is, tracking with 6 degrees of freedom, meaning the 6 variables we just mentioned.

There...

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