Computer Vision is an interdisciplinary scientific field dealing with equipping computers with high-level understanding of digital images and videos.
Automatizing tasks that the human visual system can do and going beyond human abilities are main research directions.
Computer Vision relates to and overlaps with research fields such as: image processing, image analysis, machine vision, machine learning, deep learning, artificial intelligence, signal processing, information engineering, computer graphics, neurobiology.
This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning.

The topics that will be covered are:
introduction to computer vision,
sampling and quantization,
image enhancement,
feature description,
feature detection,
geometry, detection,
motion and tracking,
traditional object recognition,
deep learning,
generative methods,
applications.

Objectives:
• Overview of the most important concepts of image formation, perception and analysis, and Computer Vision.
• Gaining own experience through practical computer and programming exercises.

Requirements:
• Basic concepts of mathematical analysis and linear algebra.
• Basic concepts of physics and optics.
• The computer exercises are based on Python and TensorFlow / Keras.
• The course language is English.