Hands-On Computer Vision with Julia

▶Book Description
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.

▶What You Will Learn
⦁ Analyze image metadata and identify critical data using JuliaImages
⦁ Apply filters and improve image quality and color schemes
⦁ Extract 2D features for image comparison using JuliaFeatures
⦁ Cluster and classify images with KNN/SVM machine learning algorithms
⦁ Recognize text in an image using the Tesseract library
⦁ Use OpenCV to recognize specific objects or faces in images and videos
⦁ Build neural network and classify images with MXNet

▶Key Features
⦁ Build a full-fledged image processing application using JuliaImages
⦁ Perform basic to advanced image and video stream processing with Julia's APIs
⦁ Understand and optimize various features of OpenCV with easy examples

▶Who This Book Is For
Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.

▶What this book covers
⦁ Chapter 1, Getting Started with JuliaImages, is about getting your first introduction to JuliaImages and ImageCore packages. We will be loading images from various sources and creating thumbnails, that is resizing and saving them back on disk in a different file format.

⦁ Chapter 2, Image Enhancement, is all about working with the ImageFiltering package. We will understand what linear and nonlinear filtering operations are and how they can be used to transform images, such as sharpening, blurring, and smoothing.

⦁ Chapter 3, Image Adjustment, will guide you through the ImageMorphology package. Morphological transformations are some simple operations based on the image shape that allow you to remove small noise, shrink objects, separate objects, and increase the object size or background space.

⦁ Chapter 4, Image Segmentation, will explore the ImageSegmentation package. Readers will learn how to use supervised and unsupervised methods to simplify or represent an image into something that is more meaningful and easier to analyze.

⦁ Chapter 5, Image Representation, will explore the ImageFeatures package. We will learn to compute compact descriptors or "features" in a form that permits comparison and matching of two images.

⦁ Chapter 6, Introduction to Neural Networks, will demonstrate the need for neural networks. We'll cover getting, preparing the data, and improving and predicting the images. This chapter will also teach you to classify datasets, training and putting it all together.

⦁ Chapter 7, Using Pre-Trained Neural Networks, will introduce you to pre-trained networks and help in predicting image classes using Inception V3 and MobileNet V2. It will also help to extract features generated by Inception V3 and MobileNet V2 and cover transfer learning using Inception V3.

⦁ Chapter 8, OpenCV, will demonstrate how to use the open source Open CV library to perform real-time computer vision analysis. We will learn to find faces on images and then track them on a video stream.