Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data

Book Synopsis: Explore mathematical computations and algorithms for image processing using popular Python tools and frameworks

Key Features
Gain practical knowledge of every image processing task with popular Python libraries
Explore topics such as pseudo-coloring, noise smoothing, and computing image descriptors
Cover 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 in social media (face detection), medical imaging (X-rays and CT scans), and security (fingerprint recognition). This book is designed to help you learn the core aspects of image processing, from essential concepts to code using the Python programming language.

The book starts by covering classical image processing techniques. You'll then go on to explore the evolution of image processing algorithms, right up to the recent advancements in image processing and computer vision with deep learning. As you progress, you'll learn how to use image processing libraries such as PIL, scikit-image, and scipy ndimage in Python. The book will further enable you to write code snippets in Python 3 and implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. You'll gradually be able to implement machine learning models using the Python library, scikit-learn. In addition to this, you'll explore deep convolutional neural networks (CNNs), such as VGG-19 with Keras, before progressing to use an end-to-end deep learning model called YOLO for object detection. Later chapters will take you through a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.

By the end of this book, you'll have learned how 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 such as Weiner in Python
Perform morphological image processing and segment images with different algorithms
Get to grips with techniques for extracting features from images and matching images
Write Python code to implement supervised machine learning and 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 image processing handbook is for computer vision engineers and machine learning developers who are well-versed in Python programming and want to delve into the various aspects and complexities of image processing. No prior knowledge of image processing techniques is required.

Table of Contents
Getting started with Image Processing Sampling Fourier TransformConvolution and Frequency domain FilteringImage EnhancementImage Enhancement using DerivativesMorphological Image ProcessingExtracting Image Features and DescriptorsImage SegmentationClassical Machine Learning Methods Learning in Image Processing - Image Classification with CNNObject Detection, Deep Segmentation and Transfer Learning Additional Problems in Image Processing     Read more