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Learn OpenCV 4 by Building Projects

You're reading from   Learn OpenCV 4 by Building Projects Build real-world computer vision and image processing applications with OpenCV and C++

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789341225
Length 310 pages
Edition 2nd Edition
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Authors (3):
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David Millán Escrivá David Millán Escrivá
Author Profile Icon David Millán Escrivá
David Millán Escrivá
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
Vinícius G. Mendonça Vinícius G. Mendonça
Author Profile Icon Vinícius G. Mendonça
Vinícius G. Mendonça
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with OpenCV FREE CHAPTER 2. An Introduction to the Basics of OpenCV 3. Learning Graphical User Interfaces 4. Delving into Histogram and Filters 5. Automated Optical Inspection, Object Segmentation, and Detection 6. Learning Object Classification 7. Detecting Face Parts and Overlaying Masks 8. Video Surveillance, Background Modeling, and Morphological Operations 9. Learning Object Tracking 10. Developing Segmentation Algorithms for Text Recognition 11. Text Recognition with Tesseract 12. Deep Learning with OpenCV 13. Other Books You May Enjoy

Detecting points using the Harris corner detector

Corner detection is a technique used to detect points of interest in an image. These interest points are also called feature points, or simply features, in computer vision terminology. A corner is basically an intersection of two edges. An interest point is basically something that can be uniquely detected in an image. A corner is a particular case of an interest point. These interest points help us characterize an image. These points are used extensively in applications such as object tracking, image classification, and visual search. Since we know that the corners are interesting, let's see how can detect them.

In computer vision, there is a popular corner detection technique called the Harris corner detector. We basically construct a 2 x 2 matrix based on partial derivatives of the grayscale image, and then analyze the...

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