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.
Table of Contents (11 chapters)
9
Assessments

First steps with the MNIST dataset

MNIST is the dataset that is always discussed first when making first steps in the world of neural networks and image classification. MNIST is a database of grayscale images of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples.

In the following activities, we will be predicting the value written on an image by building our first neural network.

Getting the data

In order to get to the process of building the neural networks quickly, we will be using the MNIST dataset, which is available in the MLDatasets.jl package. The package provides easy and user-friendly access to some of the datasets publicly available out there on the internet. If you don...