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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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9
Assessments

ORB, rotation invariant image matching

The ORB descriptor is an improved version of BRIEF; it is a mix of a FAST keypoint detector combined with a modified and enhanced version of BRIEF.

A huge benefit of using ORB over BRIEF is the use of the harris corner measure that is built into ORB. It gives an opportunity to select top N uncorrelated keypoints. On top of that, the descriptor itself is enhanced and is rotation invariant.

We not only explore ORB by using a similar example, as in the previous section, but also apply rotation to the second image. We also use the CoordinateTransformations package to rotate the image around the center, as shown in the following code:

using Images, ImageFeatures, CoordinateTransformations

img1 = Gray.(load("sample-images/cat-3417184_640.jpg"))
img2 = Gray.(load("sample-images/cat-3417184_640_watermarked.jpg"))

Since the images...

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