Book Image

Hands-On Computer Vision with Julia

By : Dmitrijs Cudihins
Book Image

Hands-On Computer Vision with Julia

By: Dmitrijs Cudihins

Overview of this book

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.

Related Content you might be interested in

Current Title:

Small book image
Hands-On Computer Vision with Julia
Dmitrijs Cudihins
Jun, 2018 | 202 pages
Small book image
Hands-On Image Processing with Python
Sandipan Dey
Nov, 2018 | 492 pages
Small book image
OpenCV 3.x with Python By Example
Prateek Joshi | Gabriel Garrido Calvo
Jan, 2018 | 268 pages
Small book image
Computer Vision with Python 3
Salil Kapur | Saurabh Kapur
Aug, 2017 | 206 pages
Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with JuliaImages
Getting Started with JuliaImages
Technical requirements
Setting up your Julia
Reading images
Saving images
Using test images
Previewing images
Cropping, scaling, and resizing
Rotating images
Summary
Questions
2
Image Enhancement
Image Enhancement
Technical requirements
Images as arrays
Applying image filters
Summary
Questions
3
Image Adjustment
Image Adjustment
Technical requirements
Image binarization
Fundamental operations
Derived operations
Summary
Questions
4
Image Segmentation
Image Segmentation
Technical requirements
Supervised methods
Unsupervised methods
Summary
Questions
Further reading
5
Image Representation
Image Representation
Technical requirements
Understanding features and descriptors
BRIEF – efficient duplicate detection method
ORB, rotation invariant image matching
BRISK – scale invariant image matching
FREAK – fastest scale and rotation invariant matching
Summary
Questions
6
Introduction to Neural Networks
Introduction to Neural Networks
Technical requirements
Introduction
First steps with the MNIST dataset
Multiclass classification with the CIFAR-10 dataset
Classifying cats versus dogs
Reusing your models
Summary
Questions
Further reading
7
Using Pre-Trained Neural Networks
Using Pre-Trained Neural Networks
Technical requirements
Introduction to pre-trained networks
Predicting image classes using Inception V3
Predicting an image class using MobileNet V2
Extracting features generated by Inception V3
Extracting features generated by MobileNet V2
Transfer learning with Inception V3
Summary
Questions
Further reading
8
OpenCV
OpenCV
Technical requirements
Troubleshooting installation of Open CV
First steps with OpenCV
Image capturing from web camera
Face detection using Open CV
Object detection using MobileNet-SSD
Summary
Questions
9
Assessments
Assessments
10
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

FREAK – fastest scale and rotation invariant matching

The last descriptor available in Julia is the FREAK descriptor, which was developed in 2012. The algorithm introduces a new keypoint descriptor inspired by the human visual system and, more precisely, the retina, coined the Fast Retina Keypoint (FREAK).

FREAKs are in general quicker to calculate with lower computational requirements. It makes them a great alternative to the methods covered in the preceding section. FREAK is considered to be the fastest scale, rotation, and noise invariant algorithm.

The code examples stay very similar to those we have already seen. First, we load the packages and images by using the following code:

using Images, ImageFeatures, CoordinateTransformations, ImageDraw, ImageView

img1 = Gray.(load("cat-3417184_640.jpg"))
img2 = Gray.(load("cat-3417184_640_watermarked.jpg&quot...
Previous Section
End of Section 7
Next Section