Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Introduction to deep learning


Deep learning is most commonly written about in scientific papers nowadays with regards to image classification and speech recognition. This is a subfield of machine learning, based on traditional neural networks and inspired by the structure of the brain. To understand this technology, it is very important to understand what a neural network is and how it works.

What is a neural network and how can we learn from data?

The neural network is inspired by the structure of the brain, in which multiple neurons are interconnected, creating a network. Each neuron has multiple inputs and multiple outputs, like a biological neuron.

This network is distributed in layers, and each layer contains a number of neurons that are connected to all the previous layer's neurons. This always has an input layer, which normally consists of the features that describe the input image or data, and an output layer, which normally consists of the result of our classification. The other middle...