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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

Linear algebra

Computer vision tools and techniques are highly dependent on linear algebraic operations. We will use see an explanation of basic to advanced operations that are required in developing CV applications.

Vectors

In a 2D plane, vectors are denoted as a point .

In this case, the magnitude of p is denoted as and is given by the following:

In Python, a vector is denoted by a one-dimensional array, as follows:

p = [1, 2, 3]

Here, the common properties required are length of the vector and magnitude of the vector, which is given as follows:

print(len(p))

>>> 3

Common vector operations are as follows:

  • Addition
  • Subtraction
  • Vector multiplication
  • Vector norm
  • Orthogonality
...
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