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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

Matching local templates


Feature point matching is the operation by which one can put in correspondence points from one image to points from another image (or points from an image set). Image points should match when they correspond to the image of the same scene element in the real world.

A single pixel is certainly not sufficient to make a decision on the similarity of two keypoints. This is why an image patch around each keypoint must be considered during the matching process. If two patches correspond to the same scene element, then one might expect their pixels to exhibit similar values. A direct pixel-by-pixel comparison of pixel patches is the solution presented in this recipe. This is probably the simplest approach to feature point matching, but as we will see, it is not the most reliable one. Nevertheless, in several situations, it can give good results.

How to do it...

Most often, patches are defined as squares of odd sizes centered at the keypoint position. The similarity between...

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