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OpenCV 4 Computer Vision Application Programming Cookbook

You're reading from   OpenCV 4 Computer Vision Application Programming Cookbook Build complex computer vision applications with OpenCV and C++

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789340723
Length 494 pages
Edition 4th Edition
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Authors (2):
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Robert Laganiere Robert Laganiere
Author Profile Icon Robert Laganiere
Robert Laganiere
David Millán Escrivá David Millán Escrivá
Author Profile Icon David Millán Escrivá
David Millán Escrivá
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Toc

Table of Contents (17) Chapters Close

Preface 1. Playing with Images FREE CHAPTER 2. Manipulating the Pixels 3. Processing Color Images with Classes 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 15. OpenCV Advanced Features 16. Other Books You May Enjoy

Matching local templates

Feature point matching is the operation by which you 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 (or object point) 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 you 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, not the most reliable one. Nevertheless, in several situations, it can give good results.

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