After successful completion of the course, students are able to plan and assess software development projects in the field of computer vision. Key findings include data collection and assessment of data quality, implementation and evaluation of results.
Overview on CV Languages, Libraries and Applications
Computer vision from an applied point of view. We will review popular programming languages as well as open and closed source software (e.g. Matlab, NumPy, OpenCV) and talk about their pros and cons. We will also talk about how to approach computer vision problems in a principle way, and how related topics such as image processing, probability theory, numerical optimization, and machine learning fit into the picture. For the most part we will talk about selected successful computer vision applications. For example, video or RGBD cameras detect faces or certain behavior in real-time, and we will see how this works. Other topics include depth and pose estimation as well as deep learning, one of the current "hot topics" in computer vision.
Mündliche Prüfung
Basic image processing and computer vision knowledge is expected (e.g. what is linear filtering? what is a camera matrix?).
