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Hands-On Computer Vision with Julia
Hands-On Computer Vision with Julia

Hands-On Computer Vision with Julia: Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence

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Profile Icon Dmitrijs Cudihins
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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Paperback Jun 2018 202 pages 1st Edition
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Arrow left icon
Profile Icon Dmitrijs Cudihins
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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Paperback Jun 2018 202 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
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Renews at $19.99p/m

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Hands-On Computer Vision with Julia

Image Enhancement

This chapter is all about working with image pixels, changing colors, and using the ImageFiltering package. We will learn how to update, convert an image to grayscale, improve color intensity, and we will learn what the linear and non-linear filtering operations are and how they can be used to transform images, such as sharpening, blurring, and smoothing.

In this chapter, we will cover the following topics:

  • Accessing pixels
  • Converting images into arrays of numbers
  • Converting arrays of numbers into colors
  • Changing color saturation
  • Converting an image to grayscale
  • Creating a custom color filter
  • Padding images
  • Blurring and sharpening images

Technical requirements

There are no any special requirements for this chapter, as long as you are following the book from Chapter 1, Getting Started with JuliaImages. If you have skipped the first chapter, please pay attention to the technical requirements there.

Images as arrays

JuliaImages are two-dimensional arrays, and every pixel can be either scalar or a one-dimensional array:

  • Grayscale images represent pixels as scalar values and they are called Gray
  • RGB is a three-dimensional array which represents each point in three different colors, such as red, green, and blue
  • PNG images with a transparent background are called RGBA

Accessing pixels

When you use a load command in Julia, it reads the image and encodes it in RGB. In the following example, we will see how Julia manages information for a single pixel:

using Images
img = load("sample-images/cats-3061372_640.jpg")
img[1:1, 1:1, :]

After Julia executes the preceding set of commands, you should expect to see the following...

Applying image filters

Now that you have learned how to apply changes to single pixels, it is time to learn about the ImageFiltering.jl package, which focuses on analyzing the information of neighborhood pixels and applying linear and non-linear filtering operations.

Padding images

At some point, you may want to add borders to the image. The ImageFiltering package extends the padarray function and allows you to add padding in different ways.

Padding with a constant value

Let's start with a simple scenario and add a 25-pixel border around the image. This is done by...

Summary

This chapter was all about working with every pixel of an image. We have learned to work with different channels and adjust them to make colors more intense and to produce a color-splash effect. We have also looked into the ImageFiltering package and used it to create pads, blurring, and sharpened images.

In the next chapter, we will move on to working with the ImageMorphology package, which is used to separate objects, remove noise, and extend the background area.

Questions

  1. What is the difference between Gray and RGB image representation?
  2. When would you use the channelview function?
  3. When would you use the permuteddimsview function?
  4. What is the difference between using the Fill function and one of the border padding effects?
  5. How did we achieve the image sharpening effect?

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Key benefits

  • • Build a full-fledged image processing application using JuliaImages
  • • Perform basic to advanced image and video stream processing with Julia's APIs
  • • Understand and optimize various features of OpenCV with easy examples

Description

Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it’s easy to use and lets you write easy-to-compile and efficient machine code. . This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You’ll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you’ll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease.

Who is this book for?

Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.

What you will learn

  • • Analyze image metadata and identify critical data using JuliaImages
  • • Apply filters and improve image quality and color schemes
  • • Extract 2D features for image comparison using JuliaFeatures
  • • Cluster and classify images with KNN/SVM machine learning algorithms
  • • Recognize text in an image using the Tesseract library
  • • Use OpenCV to recognize specific objects or faces in images and videos
  • • Build neural network and classify images with MXNet

Product Details

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Publication date : Jun 29, 2018
Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781788998796
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Product Details

Publication date : Jun 29, 2018
Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781788998796
Category :
Languages :
Tools :

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Table of Contents

10 Chapters
Getting Started with JuliaImages Chevron down icon Chevron up icon
Image Enhancement Chevron down icon Chevron up icon
Image Adjustment Chevron down icon Chevron up icon
Image Segmentation Chevron down icon Chevron up icon
Image Representation Chevron down icon Chevron up icon
Introduction to Neural Networks Chevron down icon Chevron up icon
Using Pre-Trained Neural Networks Chevron down icon Chevron up icon
OpenCV Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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granate Nov 17, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Es una aproximación muy práctica a las técnicas de clasificación de imágenes. Describe tanto las clásicas como las redes neuronales y el entrenamiento de las mismas, con un lenguaje muy asequible.
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