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
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
OpenCV 3 Computer Vision Application Programming Cookbook

You're reading from   OpenCV 3 Computer Vision Application Programming Cookbook Recipes to make your applications see

Arrow left icon
Product type Paperback
Published in Feb 2017
Publisher
ISBN-13 9781786469717
Length 474 pages
Edition 3rd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Playing with Images FREE CHAPTER 2. Manipulating Pixels 3. Processing the Colors of an Image 4. Counting the Pixels with Histograms 5. Transforming Images with Morphological Operations 6. Filtering the Images 7. Extracting Lines, Contours, and Components 8. Detecting Interest Points 9. Describing and Matching Interest Points 10. Estimating Projective Relations in Images 11. Reconstructing 3D Scenes 12. Processing Video Sequences 13. Tracking Visual Motion 14. Learning from Examples

Estimating the optical flow

When a scene is observed by a camera, the observed brightness pattern is projected on the image sensor and thus forms an image. In a video sequence, we are often interested in capturing the motion pattern, that is the projection of the 3D motion of the different scene elements on an image plane. This image of projected 3D motion vectors is called the motion field. However, it is not possible to directly measure the 3D motion of scene points from a camera sensor. All we observe is a brightness pattern that is in motion from frame to frame. This apparent motion of the brightness pattern is called the optical flow. One might think that the motion field and optical flow should be equal, but this is not always true. An obvious case would be the observation of a uniform object; for example, if a camera moves in front of a white wall, then no optical flow is generated. 

Another classical example is the illusion produced by a rotating barber pole:

Estimating the optical flow

In this case,...

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