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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
Languages
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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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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

Detecting corners in an image


When searching for interesting feature points in images, corners come out as an interesting solution. They are indeed local features that can be easily localized in an image, and in addition, they should abound in scenes of man-made objects (where they are produced by walls, doors, windows, tables, and so on). Corners are also interesting because they are two-dimensional features that can be accurately detected (even at sub-pixel accuracy), as they are at the junction of two edges. This is in contrast to points located on a uniform area or on the contour of an object; these ones would be difficult to repeatedly localize precisely on other images of the same object. The Harris feature detector is a classical approach to detecting corners in an image. We will explore this operator in this recipe.

How to do it...

The basic OpenCV function that is used to detect Harris corners is called cv::cornerHarris and is straightforward to use. You call it on an input image...

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