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OpenCV By Example

You're reading from   OpenCV By Example Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781785280948
Length 296 pages
Edition 1st Edition
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Authors (3):
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Vinícius G. Mendonça Vinícius G. Mendonça
Author Profile Icon Vinícius G. Mendonça
Vinícius G. Mendonça
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
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with OpenCV 2. An Introduction to the Basics of OpenCV FREE CHAPTER 3. Learning the Graphical User Interface and Basic Filtering 4. Delving into Histograms 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 Index

Detecting points using the Harris corner detector


Corner detection is a technique used to detect interest points in the 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, visual search, and so on. Since we know that the corners are interesting, let's see how we can detect them.

In Computer Vision, there is a popular corner detection technique called the Harris corner detector. We construct a 2 x 2 matrix based on partial derivatives of the grayscale image, and then analyze the eigenvalues. Now what does this mean? Well, let's dissect it so that we can understand it better. Let's consider a small...

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