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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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Toc

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

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 not look natural.

Consider the following image:

What is pose estimation?

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

What is pose estimation?

Now even though the location is the same, the orientation has changed. We need to have this information so that the overlaid graphics looks natural. We need to make sure that it's aligned to this orientation as well as position.

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