Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
OpenCV 4 with Python Blueprints

You're reading from   OpenCV 4 with Python Blueprints Build creative computer vision projects with the latest version of OpenCV 4 and Python 3

Arrow left icon
Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789801811
Length 366 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (4):
Arrow left icon
Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Dr. Menua Gevorgyan Dr. Menua Gevorgyan
Author Profile Icon Dr. Menua Gevorgyan
Dr. Menua Gevorgyan
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
Arsen Mamikonyan Arsen Mamikonyan
Author Profile Icon Arsen Mamikonyan
Arsen Mamikonyan
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Fun with Filters 2. Hand Gesture Recognition Using a Kinect Depth Sensor FREE CHAPTER 3. Finding Objects via Feature Matching and Perspective Transforms 4. 3D Scene Reconstruction Using Structure from Motion 5. Using Computational Photography with OpenCV 6. Tracking Visually Salient Objects 7. Learning to Recognize Traffic Signs 8. Learning to Recognize Facial Emotions 9. Learning to Classify and Localize Objects 10. Learning to Detect and Track Objects 11. Profiling and Accelerating Your Apps 12. Setting Up a Docker Container 13. Other Books You May Enjoy

Reconstructing the scene

Finally, we can reconstruct the 3D scene by making use of a process called triangulation. We are able to infer the 3D coordinates of a point because of the way epipolar geometry works. By calculating the essential matrix, we get to know more about the geometry of the visual scene than we might think. Because the two cameras depict the same real-world scene, we know that most of the 3D real-world points will be found in both images.

Moreover, we know that the mapping from the 2D image points to the corresponding 3D real-world points will follow the rules of geometry. If we study a sufficiently large number of image points, we can construct, and solve, a (large) system of linear equations to get the ground truth of the real-world coordinates.

Let's return to the Swiss fountain dataset. If we ask two photographers to take a picture of the fountain from...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image