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

Unsupervised methods

Unsupervised methods, on the other hand do not require you to mark the area or choose regions manually. The process of identifying segments is fully automatic, with some hyper-parameters available to set the minimum segment size and detail level.

The graph-based approach

This time, will be using the Felzenszwalb algorithm, which is an unsupervised and efficient graph-based approach. It was proposed by P.F Felzenszwalb and Huttenlocher in 2004 and has been actively used in computer vision since. The benefits of using the Felzenszwalb algorithm are as follows:

  • A small number of hyperparameters
  • Fast and linear execution time
  • Preserves details in low variability areas

Julia implements Felzenszwalb algorithms...