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OpenCV with Python By Example

You're reading from   OpenCV with Python By Example Build real-world computer vision applications and develop cool demos using OpenCV for Python

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781785283932
Length 296 pages
Edition 1st Edition
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Author (1):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Table of Contents (14) Chapters Close

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. Creating a Panoramic Image 7. Seam Carving 8. Detecting Shapes and Segmenting an Image 9. Object Tracking 10. Object Recognition 11. Stereo Vision and 3D Reconstruction 12. Augmented Reality Index

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:

How to track planar objects?

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

How to track planar objects?

Let's tilt the cardboard:

How to track planar objects?

As we can see, the cardboard 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 those feature points. If we extract the feature points from the preceding image, it will look like this:

How to track planar objects?

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.

How to track planar objects?

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...

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