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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

Introduction to probability theory

We have studied probability in several courses through university or elsewhere. In this section, the aim is to fill in the gaps, so that computer vision algorithms that require probability theory can be easily built upon. The motivation to use probability theory in computer vision is to model uncertainty.

What are random variables?

Random variables are used to define the possibilities of an event in terms of real numbers. The values it can represent are random and, by applying certain assumptions, we can restrict it to given range. To get started with random variables, we need to either compute a function that approximates its behavior or assume and prove our hypothesis function through experimentation...

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