Written exam

The goal of computer vision is to compute geometric and semantic properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an object, determining how things are moving and recognizing objects or scenes. This course will provide an
introduction to computer vision, with topics including image formation, camera models, camera calibration, feature detection and matching, motion estimation, geometry reconstruction, object detection and tracking, and scene understanding. Applications
include building 3D maps, creating virtual avatars, image search,
organizing photo collections, human computer interaction, video surveillance, self-driving cars, robotics, virtual and augmented reality, simulation, medical imaging, and mobile computer vision. Modern computer vision relies heavily on machine learning in particular deep learning and graphical models. This course therefore assumes prior knowledge of deep learning (e.g., deep learning lecture) and introduces the basic concepts of graphical models and structured prediction where needed.

Course Website: https://uni-tuebingen.de/de/203146

Students gain an understanding of the theoretical and practical concepts of computer vision including image formation, camera models, feature detection, multiple view geometry, 3D
reconstruction, motion estimation, object recognition, scene understanding and structured prediction using deep neural networks
and graphical models. After this course, students should be able to understand
and apply the basic concepts of computer vision in practice, develop and train
computer vision models, reproduce research results and conduct original research
in this area.

Related literature will be listed throughout the lecture.