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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

Summary

In this chapter, we learned how to install the different library files of Python, Keras, and TensorFlow. In order to use several code snippets in further chapters, these libraries will be sufficient. We also had a look at different datasets like ImageNet, MNIST, CIFAR-10, MSCOCO and TUM RGBD datasets. These datasets are the backbone for computer vision applications since the ability of several software that we develop directly depends on the availability of these datasets.

In next chapter, we will begin with more in-depth image analysis by introducing different types of filters and also learn transformations on image such as translation, rotation or affine.

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