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OpenCV 3.x with Python By Example

You're reading from   OpenCV 3.x with Python By Example Make the most of OpenCV and Python to build applications for object recognition and augmented reality

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788396905
Length 268 pages
Edition 2nd Edition
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Authors (2):
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Gabriel Garrido Calvo Gabriel Garrido Calvo
Author Profile Icon Gabriel Garrido Calvo
Gabriel Garrido Calvo
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Contributors
Packt Upsell
Preface
1. Applying Geometric Transformations to Images FREE CHAPTER 2. Detecting Edges and Applying Image Filters 3. Cartoonizing an Image 4. Detecting and Tracking Different Body Parts 5. Extracting Features from an Image 6. Seam Carving 7. Detecting Shapes and Segmenting an Image 8. Object Tracking 9. Object Recognition 10. Augmented Reality 11. Machine Learning by an Artificial Neural Network 1. Other Books You May Enjoy

What is pose estimation?


Before we proceed, we need to understand how to estimate the camera pose. This is a very critical step in an augmented reality system and we need to get it right if we want our experience to be seamless. In the world of augmented reality, we overlay graphics on top of an object in real time. In order to do that, we need to know the location and orientation of the camera, and we need to do it quickly. This is where pose estimation becomes very important. If you don't track the pose correctly, the overlaid graphics will look unnatural.

Consider the following image:

The arrow indiactes that the surface is normal. Let's say the object changes its orientation:

Now, even though the location is the same, the orientation has changed. We need to have this information so that the overlaid graphics look natural. We need to make sure that the graphic is aligned with this orientation and position.

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