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

Extracting features generated by Inception V3

The first layers of any neural network are basically responsible for identifying low-level features, such as edges, colors, and blobs, but the last layers are usually very specific to the task they are trained for.

Because pre-trained networks are usually trained on a very large dataset such as ImageNet, which contains over 10 million images, it makes those features very generic and possible to be reused for other models.

In the following activity, we will learn how to extract the features from the last activation and use them for solving the CIFAR-10 problem, where we previously achieved around 70% accuracy.

Preparing the network

We assume that you have successfully run the example...