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Book details
Format: pdf
File Size: 112 MB
Print Length: 494 pages
Publisher: Packt Publishing; 1 edition (30 November 2018)
Sold by: Amazon Asia-Pacific Holdings Private Limited
Language: English
ASIN: B07J664F9S
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.
Key Features
Practical coverage of every image processing task with popular Python libraries
Includes topics such as pseudo-coloring, noise smoothing, computing image descriptors
Covers popular machine learning and deep learning techniques for complex image processing tasks
Book Description
Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.
The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.
By the end of this book, we will have learned to implement various algorithms for efficient image processing.
What you will learn
Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python
Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python
Do morphological image processing and segment images with different algorithms
Learn techniques to extract features from images and match images
Write Python code to implement supervised / unsupervised machine learning algorithms for image processing
Use deep learning models for image classification, segmentation, object detection and style transfer
Who this book is for
This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.
Table of Contents
Getting started with Image Processing
Sampling Fourier Transform
Convolution and Frequency domain Filtering
Image Enhancement
Image Enhancement using Derivatives
Morphological Image Processing
Extracting Image Features and Descriptors
Image Segmentation
Classical Machine Learning Methods
Learning in Image Processing – Image Classification with CNN
Object Detection, Deep Segmentation and Transfer Learning
Additional Problems in Image Processing
