After successful completion of the course, students are able to identify, explain and contrast the most important theories, principles, concepts and algorithms of Computer Vision. Their knowledge and understanding correspond to the state of the art literature in the field of computer vision. They are able to apply appropriate formal-mathematical methods for modeling, abstraction, solution finding and evaluation, and gain problem-formulation and problem-solving skills.
How can computers understand the visual world of humans?
This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. Topics include: Structure of Images, Texture, Scenes, and Context, Feature Based Alignment, Stereo Correspondence, Structure from Motion, Image Stitching, Computational Photography, Image Based Rendering, and Baysian Modeling for Object Recognition. This course is designed for students interested in vision, artificial intelligence, or machine learning. The course offers a broad introduction to the field, the current problems and theories, the basic mathematics, and some interesting algorithms and the possibility to apply the contents learned direct in exercises.
Frontal presentation and written exam, independent solving and scientific discussion of subject-related examples and submissions. The course consists of a lecture part and an exercise part. The contents and concepts described are explained in the context of the lecture unit and practically tested and applied in the practice section.
