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OpenCV 3 Computer Vision Application Programming Cookbook

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

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Product type Paperback
Published in Feb 2017
Publisher
ISBN-13 9781786469717
Length 474 pages
Edition 3rd Edition
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Author (1):
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Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
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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,...

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