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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

How to track planar objects


Now that you understand what pose estimation is, let's see how you can use it to track planar objects. Let's consider the following planar object:

Now, if we extract feature points from this image, we will see something like this:

Let's tilt the cardboard box:

As we can see, the cardboard box is tilted in this image. Now, if we want to make sure our virtual object is overlaid on top of this surface, we need to gather this planar tilt information. One way to do this is by using the relative positions of the feature points. If we extract the feature points from the preceding image, it will look like this:

As you can see, the feature points got closer horizontally on the far end of the plane as compared to the ones on the near end:

So, we can utilize this information to extract the orientation information from the image. If you remember, we discussed perspective transformation in detail when we were discussing geometric transformations, as well as panoramic imaging. All...

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