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Hands-On Computer Vision with Julia : Build Complex Applications with Advanced Julia Packages for Image Processing, Neural Networks, and Artificial Intelligence.
This book 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 of its ease of use and the fact that it lets you write easy-to-compile and efficient machine code.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: 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.
- Chapter 2: Image EnhancementTechnical 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; Chapter 3: Image Adjustment; Technical requirements; Image binarization; Fundamental operations; Image erosion; Object separation using erosion.
- Image preparation for text recognitionImage dilation; Merging almost-connected objects; Highlighting details; Derived operations; Image opening; Image closing; Top-hat and bottom-hat operation; Adjusting image contrast; Summary; Questions; Chapter 4: 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; Chapter 5: Image Representation; Technical requirements.
- Understanding features and descriptorsFAST 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; Chapter 6: Introduction to Neural Networks; Technical requirements; Introduction; The need for neural networks; The need for MXNet; First steps with the MNIST dataset.
- Getting the dataPreparing 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.