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Hands-On Computer Vision with Julia

Hands-On Computer Vision with Julia

By : Dmitrijs Cudihins
4 (1)
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Hands-On Computer Vision with Julia

Hands-On Computer Vision with Julia

4 (1)
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.
Table of Contents (11 chapters)
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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
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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
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Image Representation

The ImageFeatures package is used to determine features or descriptors of an image or its area and is later used in a different set of applications, such as object recognition, detection, image matching, and overlaying.

The following topics will be covered in this chapter:

  • Corner detection using the FAST algorithm
  • Corner detection using the imcorner function
  • The BRIEF algorithm
  • ORB, the rotation invariant algorithm
  • The BRISK, rotation, and scale invariant algorithms
End of Section 1
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