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Practical Computer Vision

You're reading from   Practical Computer Vision Extract insightful information from images using TensorFlow, Keras, and OpenCV

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788297684
Length 234 pages
Edition 1st Edition
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Author (1):
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Abhinav Dadhich Abhinav Dadhich
Author Profile Icon Abhinav Dadhich
Abhinav Dadhich
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Table of Contents (12) Chapters Close

Preface 1. A Fast Introduction to Computer Vision FREE CHAPTER 2. Libraries, Development Platform, and Datasets 3. Image Filtering and Transformations in OpenCV 4. What is a Feature? 5. Convolutional Neural Networks 6. Feature-Based Object Detection 7. Segmentation and Tracking 8. 3D Computer Vision 9. Mathematics for Computer Vision 10. Machine Learning for Computer Vision 11. Other Books You May Enjoy

Harris Corner Detection

We start feature point detection using the Harris Corner Detection[1] technique. In this, we begin with choosing a matrix, termed a window, which is small in size as compared to the image size.

The basic idea is to first overlay chosen window on the input image and observe only the overlayed region from the input image. This window is later shifted over the image and the new overlayed region is observed. In this process, there arise three different cases:

  • If there is a flat surface, then we won't be able to see any change in the window region irrespective of the direction of movement of the window. This is because there is no edge or corner in the window region.
  • In our second case, the window is overlayed on edge in the image and shifted. If the window moves along the direction of the edge, we will not be able to see any changes in the window. While...
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