Explore the world of image processing and CV (computer vision) with Python from fundamental concepts to deep learning
Key Features
- Get to grips with image enhancement, restoration, segmentation, feature-extraction, classification, object detection, and image synthesis
- Benefit from hands-on guidance for vital image processing tasks using Python libraries
- Familiarize yourself with various techniques, spanning classical, machine learning, and cutting-edge deep learning methods
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Being able to process, extract information from, and understand images quickly has become a critical part of real-world applications in security, payment, healthcare, advertising, and other sectors. This book covers a wide range of topics, following a highly practical approach that guides you through key image processing concepts and algorithms, helping you use leading Python libraries to implement them effectively.
As you progress, you’ll gain proficiency in writing Python 3 code and implementing complex image processing and computer vision algorithms to solve problems in image enhancement, restoration, denoising, segmentation, classification, and object detection. You’ll work with libraries such as PIL, scikit-image, SciPy ndimage, and OpenCV. You’ll also learn to apply machine learning models with scikit-learn and explore deep learning techniques using CNNs (for example, ResNet and YOLO) with TensorFlow, Keras, and PyTorch. The final chapters guide you through advanced topics such as image-to-image translation, anisotropic diffusion, and generative art.
By the end of this book, you will be well versed in image processing and ready to solve a variety of commonly occurring problems.
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, and 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 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 and applied researchers interested in CV, image processing, ML and deep learning, especially those who are proficient in Python programming and want to learn how to implement different algorithms for image processing. Working knowledge of Python is a must.
Table of Contents
- Getting started with Image Processing
- More Image Manipulation
- Sampling and Fourier Transform
- Image Enhancement
- Image Enhancements using Derivatives
- Morphological Image Processin
- Extracting Image Features and Descriptors
- Image Segmentation
- Classical Machine Learning Methods
- Deep Learning in Image Processing - Image Classification with Deep Neural Networks
- Object Detection, Deep Segmentation and Transfer Learning
- Additional Problems in Image Processing
Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.
