- When: Wed 10.00 – 12.00 c.t.
- Where: Seminarraum 183/2 (map)
This course encourages students to select and implement a computer vision project of their choice. Students are free to develop in any programming language they like and to use any publicly available library they want. The only requirement is that the effort for developing the chosen application is in line with the ECTS of this course. The goal is to encourage students to investigate a selected computer vision topic in detail, and to allow them to improve their computer vision programming skills.
Sensor hardware is provided. The available hardware e.g. includes Kinect sensors (both versions), Orbec sensors as well as a thermal imaging camera, a network of multiple IP cameras with overlapping views, a 3D printer and other.
Participants are encouraged to select a computer vision problem according to their interests. Some examples are presented as part of the first lecture, and the lecturers are happy to help participants choose their topics. It is also possible to implement an internship topic, partially or fully in groups.
An example for a project that was developed for this course is Wurstify.
This is not a general programming course; students are expected to be able to develop software on their own, and they should be familiar with a programming language suitable for computer vision development (e.g. Matlab, Python, C++). Basic image processing and computer vision knowledge is expected. Experience in computer vision development is recommended but not required.
Project 1: Analysis of historical videos/films
Project 2: Bee image analysis: from image acquisition to object detection
Project 3: 3d printing workflow
Project 4: Mobile vision / mobile image analysis tasks: from mobile object recognition to background substraction; Deep Learning on mobile devices
Project 04: Frame Border Detection in historical videos (Pointner Bernhard)
The initial, midterm, and final presentations account for 5%, 5% and 10% of the grade, respectively. The project, which must include a written report, is worth 80% of the grade.
We recommend the associated lecture that covers software and resources for computer vision development as well as selected computer vision applications.
see in TISS: 183.586 Computer Vision Systems Programming –> Einzeltermine