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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

Libraries, Development Platform, and Datasets

In this chapter, we will be setting up a development environment to help run codes for the book as well as for generic development and also introduce various datasets for computer vision. Since there are several standard libraries which are used both for studying computer vision and in the industry for deployment, it becomes trivial to also use them in learning path. As we study the various sub-topics of computer vision in further chapters, we will be able to directly implement the codes introduced then rather than getting stuck in installations and other library dependencies.

This chapter is divided into two major sections:

  • Firstly we will be setting up python based environment such Anaconda
  • We will then setup OpenCV and various forms of its installations
  • For deep learning, we will also setup Keras and TensorFlow
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