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Learning OpenCV 5 Computer Vision with Python

You're reading from   Learning OpenCV 5 Computer Vision with Python Tackle computer vision and machine learning with the newest tools, techniques and algorithms

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781803230221
Length
Edition 4th 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
Author Profile Icon Joseph Howse
Joseph Howse
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Table of Contents (12) Chapters Close

1. Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning FREE CHAPTER
2. Setting Up OpenCV 3. Handling Files, Cameras, and GUIs 4. Processing Images with OpenCV 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. OpenCV Applications at Scale 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 (its position and orientation) in a 3D space. Typically, the pose is expressed 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 (that is, orientation).

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. With any fewer than 6 variables, it would...

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