Title Page Copyright and Credits Hands-On Computer Vision with Julia Packt Upsell Why subscribe? PacktPub.com Contributors About the author About the reviewer Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews Getting Started with JuliaImages Technical requirements Setting up your Julia Installing packages Reading images Reading a single image from disk Reading a single image from a URL Reading images in a folder Saving images Using test images Previewing images Cropping, scaling, and resizing Cropping an image Resizing an image Scaling an image Scaling by percentage Scaling to a specific dimension Scaling by two-fold Rotating images Summary Questions Image Enhancement Technical requirements Images as arrays Accessing pixels Converting images into arrays of numbers Converting arrays of numbers into colors Changing color saturation Converting an image to grayscale Creating a custom color filter Applying image filters Padding images Padding with a constant value Padding by duplicating content from an image Blurring images Sharpening images Summary Questions Image Adjustment Technical requirements Image binarization Fundamental operations Image erosion Object separation using erosion Image preparation for text recognition Image dilation Merging almost-connected objects Highlighting details Derived operations Image opening Image closing Top-hat and bottom-hat operation Adjusting image contrast Summary Questions Image Segmentation Technical requirements Supervised methods Seeded region growing Identifying a simple object Identifying a complex object Unsupervised methods The graph-based approach The fast scanning approach Helper functions Summary Questions Further reading Image Representation Technical requirements Understanding features and descriptors FAST corner detection Corner detection using the imcorner function Comparing performance BRIEF – efficient duplicate detection method Identifying image duplicates Creating a panorama from many images ORB, rotation invariant image matching BRISK – scale invariant image matching FREAK – fastest scale and rotation invariant matching Running face recognition Summary Questions Introduction to Neural Networks Technical requirements Introduction The need for neural networks The need for MXNet First steps with the MNIST dataset Getting the data Preparing the data Defining a neural network Fitting the network Improving the network Predicting new images Putting it all together Multiclass classification with the CIFAR-10 dataset Getting and previewing the dataset Preparing the data Starting with the linear classifier Reusing the MNIST architecture Improving the network  Accuracy – why at almost 70 Putting it all together Classifying cats versus dogs Getting and previewing the dataset Creating an image data iterator Training the model Putting it all together Reusing your models Saving the model Loading the model Summary Questions Further reading Using Pre-Trained Neural Networks Technical requirements Introduction to pre-trained networks Transfer learning MXNet Model Zoo Predicting image classes using Inception V3 Setting up the Inception V3 environment Loading the network Preparing the datasets Running predictions Expected performance Putting it all together Predicting an image class using MobileNet V2 Setting up the environment Loading the network Preparing the datasets Running the predictions Expected performance Putting it all together Extracting features generated by Inception V3 Preparing the network Removing the last Softmax and FullyConnected layers Predicting features of an image Saving the network to disk Extracting features generated by MobileNet V2 Preparing the network Removing the last Softmax and FullyConnected layers Predicting features of an image Saving the network to disc Putting it all together Transfer learning with Inception V3  Getting the data Preparing the dataset Extracting features Creating a new network Training and validating the results Summary Questions Further reading OpenCV Technical requirements Troubleshooting installation of Open CV Troubleshooting installation on macOS First steps with OpenCV Updating OpenCV package source code Defining Open CV location Testing whether OpenCV works Working with images Converting OpenCV Mat to Julia images Reading images Saving images Destroying the object Image capturing from web camera Face detection using Open CV Object detection using MobileNet-SSD Summary Questions Assessments Other Books You May Enjoy Leave a review - let other readers know what you think