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Artificial Vision and Language Processing for Robotics
Artificial Vision and Language Processing for Robotics

Artificial Vision and Language Processing for Robotics: Create end-to-end systems that can power robots with artificial vision and deep learning techniques

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Profile Icon Morena Alberola Profile Icon Garay Maestre Profile Icon Molina Gallego
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€29.99
Paperback Apr 2019 356 pages 1st Edition
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Arrow left icon
Profile Icon Morena Alberola Profile Icon Garay Maestre Profile Icon Molina Gallego
Arrow right icon
€29.99
Paperback Apr 2019 356 pages 1st Edition
eBook
€23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m

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Artificial Vision and Language Processing for Robotics

Chapter 2. Introduction to Computer Vision

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Explain the impact of artificial intelligence and computer vision

  • Deploy some of the basic computer vision algorithms

  • Develop some of the basic machine learning algorithms

  • Construct your first neural network

Note

This chapter covers an introduction to computer vision followed by a few important basic computer vision and machine learning algorithms.

Introduction


Artificial Intelligence (AI) is changing everything. It tries to mimic human intelligence in order to achieve different tasks.

The section of AI that deals with images is called computer vision. Computer vision is an interdisciplinary scientific field that tries to mimic human eyes. It not only makes sense out of the pixels that are extracted from an image, but also gains a higher level of understanding from that specific image by performing automated tasks and using algorithms.

Some of these algorithms are better at object recognition, recognizing faces, classifying images, editing images, and even generating images.

This chapter will begin with an introduction to computer vision, starting with some of the most basic algorithms and an exercise to put them into practice. Later, an introduction to machine learning will be given, starting from the most basic algorithms to neural networks, involving several exercises to strengthen the knowledge acquired.

Basic Algorithms in Computer Vision


In this topic, we will be addressing how images are formed. We will introduce a library that is very useful for performing computer vision tasks and we will learn about the workings of some of these tasks and algorithms and how to code them.

Image Terminology

To understand computer vision, we first need to know how images work and how a computer interprets them.

A computer understands an image as a set of numbers grouped together. To be more specific, the image is seen as a two-dimensional array, a matrix that contains values from 0 to 255 (0 being for black and 255 for white in grayscale images) representing the values of the pixels of an image (pixel values), as shown in the following example:

Figure 2.1: Image representation without and with pixel values

In the image on the left-hand side, the number 3 is shown in a low resolution. On the right-hand side, the same image is shown along with the value of every pixel. As this value rises, a brighter color...

Introduction to Machine Learning


Machine learning (ML) is the science of making computers learn from data without stating any rules. ML is mostly based on models that are trained with a lot of data, such as images of digits or features of different objects, with their corresponding labels, such as the number of those digits or the type of the object. This is called supervised learning. There are other types of learning, such as unsupervised learning and reinforcement learning, but we will be focusing on supervised learning. The main difference between supervised learning and unsupervised learning is that the model learns clusters from the data (depending on how many clusters you specify), which are translated into classes. Reinforcement learning, on the other hand, is concerned with how software agents should take action in an environment in order to increase a reward that is given to the agent, which will be positive if the agent is performing the right actions and negative otherwise.

In...

Summary


Computer vision is a big field within AI. By understanding this field, you can achieve results such as extracting information from an image or generating images that look just like they do in real life, for example. This chapter has covered image preprocessing for feature extraction using the OpenCV library, which allows easy training and prediction for machine learning models. Some basic machine learning models have also been covered, such as decision trees and boosting algorithms. These served as an introduction to machine learning and were mostly used to play around. Finally, neural networks were introduced and coded using Keras and TensorFlow as a backend. Normalization was explained and put into practice, along with dense layers, though convolutional layers are known to work better with images than dense layers do, and they will be explained later in the book.

Concepts for avoiding overfitting were also covered, and toward the end, we used the model to make predictions and put...

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Key benefits

  • Study ROS, the main development framework for robotics, in detail
  • Learn all about convolutional neural networks, recurrent neural networks, and robotics
  • Create a chatbot to interact with the robot

Description

Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.

Who is this book for?

Artificial Vision and Language Processing for Robotics is for robotics engineers who want to learn how to integrate computer vision and deep learning techniques to create complete robotic systems. It will prove beneficial to you if you have working knowledge of Python and a background in deep learning. Knowledge of the ROS is a plus.

What you will learn

  • Explore the ROS and build a basic robotic system
  • Understand the architecture of neural networks
  • Identify conversation intents with NLP techniques
  • Learn and use the embedding with Word2Vec and GloVe
  • Build a basic CNN and improve it using generative models
  • Use deep learning to implement artificial intelligence(AI)and object recognition
  • Develop a simple object recognition system using CNNs
  • Integrate AI with ROS to enable your robot to recognize objects
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Length: 356 pages
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Table of Contents

9 Chapters
Fundamentals of Robotics Chevron down icon Chevron up icon
Introduction to Computer Vision Chevron down icon Chevron up icon
Fundamentals of Natural Language Processing Chevron down icon Chevron up icon
Neural Networks with NLP Chevron down icon Chevron up icon
Convolutional Neural Networks for Computer Vision Chevron down icon Chevron up icon
Robot Operating System (ROS) Chevron down icon Chevron up icon
Build a Text-Based Dialogue System (Chatbot) Chevron down icon Chevron up icon
Object Recognition to Guide a Robot Using CNNs Chevron down icon Chevron up icon
Computer Vision for Robotics Chevron down icon Chevron up icon
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