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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

Image formation

The basic camera model is a pinhole camera, though the real-world cameras that we use are far more complex models. A pinhole camera is made up of a very small slit on a plane that allows the formation of an image as depicted in the following figure:

This camera converts a point in the physical world, often termed the real world, to a pixel on an image plane. The conversion follows the transformation of the three-dimensional coordinate to two-dimensional coordinates. Here in the image plane, the coordinates are denoted as where , Pi is any point on an image. In the physical world, the same point is denoted by , where Pw is any point in the physical world with a global reference frame.

Pi(x', y') and Pw(x, y, z) can be related as, for an ideal pin hole camera:

Here, f is focal length of the camera.

For further discussion on geometry of image formation...

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