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

Fundamental operations

Mathematical morphology is a set of non-linear operations and techniques related to the shape or features of an image. There are two base elements of image morphology:

  • A binary, or grayscale, image
  • The structuring element

We have already discussed the prerequisites regarding image and color scheme, but the structuring element is new to us. The structuring element is usually a 3x3 binary block that slides over the image and updates it. There are two fundamental operations achieved by sliding the structuring element:

  • Image erosion: Removes pixels from object boundaries
  • Image dilation: Adds pixels to the borders of objects in an image
At the time of writing, erode and dilate from the ImageMorphology package support 3x3 structuring elements only. The 3×3 square is the most popular structuring element used in morphological operations. A larger structuring...