Practical Course: GPU Programming in Computer Vision (6h / 10 ECTS)
WS 2011/12, TU München
Location: Seminar room 02.09.023 / Lab 02.13.008
Time and Date:
- 6 days with 7-8 hours lectures and supervised practical training each day:
- Mo-Fr, March 12-16 and Mo, March 19, 2012
- After this week students will have time to finish their project independently and to prepare the project presentation (workload approx. 5 days).
The course will be held in English, if desired.
- send an e-mail with your name, student id and major field of study to Martin Oswald. Please also indicate that you want to register for the course in WS2011/12.
- register in TUMonline for the examination in this course. (Note: There is no course enrolment in TUMonline, you only have to register for the course examination!)
There is currently no more space available. You can still write an email to put your name on a waiting list.
Alternatively, you can already register for the
Note to avoid any confusion: This course takes place at the end of the winter semester 2011/12 during the semester break. It will also be credited to the winter semester 2011/12!
Start: Mo, March 12, 2012, 11:00h - seminar room 02.09.023
Requirements: Knowledge in C, basic mathematics
Number of Students: up to 16
The goal of this course is to provide an introduction into the NVIDIA CUDA Framework with the C programming language extension.
During the implementation of basic computer vision algorithms students will gradually learn more how to harness the power of GPU computing.
Although we assume good knowledge of the C language and basic mathematics, no further prior knowledge about CUDA, or computer vision topics will be required.
During the course students will learn how to program GPUs with CUDA. Afterwards the students will start to implement more sophisticated computer vision algorithms within a student project. The course finishes with the presentation of the project results.
- Introduction to Parallel Computing
- Introduction to CUDA
- Implementation of Basic Algorithms with CUDA (e.g. convolution, diffusion)
- Student Project: real-time optical flow estimation
- 30 percent: basic implementations
- 50 percent: student project
- 20 percent: project presentation