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Index
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
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