Book Image

OpenCV 3.x with Python By Example - Second Edition

By : Gabriel Garrido Calvo, Prateek Joshi
Book Image

OpenCV 3.x with Python By Example - Second Edition

By: Gabriel Garrido Calvo, Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

How to define multi-layer perceptrons (MLP)

MLP is a branch of ANNs widely used in pattern recognition because of its ability of identify patterns within noisy or unexpected environments. MLP can be used to implement supervised and unsupervised learning (both of them were discussed Chapter 9, Object Recognition). In addition to that, MLP can also be used to implement another kind of learning such as reinforcement learning inspired by behavioral psychology, where the network learning is adjusted using reward/punishment actions.

Defining an ANN-MLP consist of deciding the structure of the layers that will compose our net, and how many nodes will be in each of them. Firstly, we need to decide what the goal of our network is. For instance, we could implement an object recognizer, in which case, the number of nodes belonging to the output layer will be the same as the number of different objects we want to identify. Simulating the example from Chapter 9, Object Recognition, in the case of recognizing...