Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.

This course offers an in-depth exploration of deep generative models, focusing on foundational concepts, practical implementation, and the latest advancements in the field. Students will learn about key models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, gaining both theoretical knowledge and hands-on experience.

It is the objective of this lecture to introduce the basic concepts usedin Medical Image Analysis. In particular the lecture focuses on shaperepresentation schemes, segmentation techniques, machine learning based predictive models and various image registration methods commonly used in Medical Image Analysis applications.