The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises.
The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature.
Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition
Prerequisites / Notice
It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.
Performance assessment information (valid until the course unit is held again)
The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examination
oral 20 minutes
Additional information on mode of examination
The grade will be determined by a) an oral exam and b) assignments performed during the semester. Specifically, your final grade will be 2/3 oral exam and 1/3 assignments, if your grade on the oral exam is 3.5 or higher, only your oral exam otherwise. Students have the opportunity to prepare the questions for up to 1 h before the oral exam.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.