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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 neural networks

Neural networks have been here for quite some time, with initial papers arriving more than a few decades ago. The recent popularity is due to the availability of better software for algorithms and proper hardware to run them. Initially, neural networks were motivated by how humans perceive the world and were modeled according to biological neuron functions. This was continuously modified over the course of time and has since been evolving to get a better performance.

A simple neural network

A simple neural net consists of a node which takes in an input or a list of inputs and performs a transformation. An example is shown in the following figure:

Mathematically, it takes the inputs x and applies...

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