Over 50 problems solved with classical algorithms + ML / DL models KEY FEATURES ● Problem-driven approach to practice image processing. ● Practical usage of popular Python libraries: Numpy, Scipy, scikit-image, PIL and SimpleITK.● End-to-end demonstration of popular facial image processing challenges using MTCNN and Microsoft's Cognitive Vision APIs. DESCRIPTION This book starts with basic Image Processing and manipulation problems and demonstrates how to solve them with popular Python libraries and modules. It then concentrates on problems based on Geometric image transformations and problems to be solved with Image hashing. Next, the book focuses on solving problems based on Sampling, Convolution, Discrete Fourier transform, Frequency domain filtering and image restoration with deconvolution. It also aims at solving Image enhancement problems using different algorithms such as spatial filters and create a super resolution image using SRGAN.Finally, it explores popular facial image processing problems and solves them with Machine learning and Deep learning models using popular python ML / DL libraries. WHAT YOU WILL LEARN ● Develop strong grip on the fundamentals of Image Processing and Image Manipulation.● Solve popular Image Processing problems using Machine Learning and Deep Learning models.● Working knowledge on Python libraries including numpy, scipy and scikit-image.● Use popular Python Machine Learning packages such as scikit-learn, Keras and pytorch.● Live implementation of Facial Image Processing techniques such as Face Detection / Recognition / Parsing dlib and MTCNN. WHO THIS BOOK IS FOR This book is designed specially for computer vision users, machine learning engineers, image processing experts who are looking for solving modern image processing/computer vision challenges. About the AuthorSandipan 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, such as 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 also authored a couple of Image Processing books, published from an international publication house. He has earned certifications from 100+ MOOCs on data science and related courses.
