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Hands-On Computer Vision with Julia

You're reading from   Hands-On Computer Vision with Julia Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788998796
Length 202 pages
Edition 1st Edition
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Author (1):
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Dmitrijs Cudihins Dmitrijs Cudihins
Author Profile Icon Dmitrijs Cudihins
Dmitrijs Cudihins
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with JuliaImages FREE CHAPTER 2. Image Enhancement 3. Image Adjustment 4. Image Segmentation 5. Image Representation 6. Introduction to Neural Networks 7. Using Pre-Trained Neural Networks 8. OpenCV 9. Assessments
10. Other Books You May Enjoy

BRISK – scale invariant image matching

BRISK is another detector available in Julia. The most significant benefit of BRISK is that it is scale and rotation invariant. Scale invariance comes with computational costs, which makes it slightly slower than ORB.

BRISK also allows using any other keypoints descriptor provided by, for example, imcorner, such as harris. We continue using FAST as in the preceding examples.

So, let's start using BRISK. As always, we start by loading the packages and images, as shown in the following code:

using Images, ImageFeatures, CoordinateTransformations, ImageDraw, ImageView

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

We have set the target to demonstrate that BRISK is scale and rotation invariant. Then, we apply two types of transformations...

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