Hands-On Image Processing with Python: Advanced methods for analyzing, transforming, and interpreting digital images - Tapa blanda

Sinopsis

Explore the world of image processing and CV with Python. From fundamental concepts to deep learning, this comprehensive guide covers a wide range of topics, including image manipulation, feature extraction, object detection, and advanced algorithms.

Key Features

• Covers image enhancement, restoration, segmentation, feature-extraction, classification, object detection, and image synthesis
• Provides hands-on guidance for vital image processing tasks using Python libraries
• Offers various techniques, spanning classical, machine learning, and cutting-edge deep learning methods

Book Description

Being able to process, extract information and understand an image quickly has become critical to many applications such as security, payment, healthcare, advertising, and so on. This is where this book comes in.

The book will start with the basics and guide you to go to on an advanced level by providing python reproducible implementations throughout the book. You will be proficient to write code snippets in Python 3 and quickly implement complex image processing and computer vision algorithms to solve problems in image enhancement, restoration, denoising, segmentation, classification, object detection and so on, using image processing libraries such as PIL, scikit-mage, scipy ndimage, opencv-python. You will learn to use ML models using scikit-learn and explore recent advances with deep learning models with CNNs (e.g., ResNet, Yolo) using tensorflow, keras, pytorch. You will also learn a few advanced problems such as image-to-image translation, anisotropic diffusion, generative arts. By the end of this book, you will be highly hands-on to solve problems on image processing.

This book follows a highly practical approach which that will take its readers through a set of image- processing concepts/algorithms and help them learn, in detail, how to use leading python library functions to implement these algorithms.

What you will learn

• Gain expertise in tackling digital image processing problems from diverse perspectives
• Explore the intricacies of image transformation, enhancement, spatial and frequency domain filters, along with morphological image processing
• Dive into the realm of image restoration and inpainting
• Harness the power of ML models for image classification and object detection
• Embark on a journey into the realm of deep learning, including CNNs, attention mechanisms, autoencoders, GANs, and transfer learning
• Push the boundaries with advanced topics such as image captioning, pseudo coloring, 3D image processing, generative art, and quantum image processing
• Apply your knowledge to real-world domains, including medical imaging, security systems, and remote sensing applications

Who this book is for

This book is for Engineers / Applied Researchers interested in CV, Image Processing, ML and Deep Learning, especially for the readers who are good with Python programming and want to learn and implement different algorithms for image processing: from concept to implementation and become well-acquainted with different approaches for solving image processing / computer vision problems.

Table of Contents

1. Getting started with Image Processing
2. Sampling Fourie Transform
3. Convolution and Frequency domain Filtering
4. Image Enhancement
5. Image Enhancements using Derivatives
6. Morphological Image Processin
7. Extracting Image Features and Descriptors
8. Image Segmentation
9. Classical Machine Learning Methods
10. Deep Learning in Image Processing - Image Classification with Deep Neural Networks
11. Object Detection, Deep Segmentation and Transfer Learning
12. Additional Problems in Image Processing

"Sinopsis" puede pertenecer a otra edición de este libro.