Practical Course: GPU Programming in Computer Vision (6h / 10 ECTS)
SS 2012, TU München
Location: Lab 02.05.014
Time and Date:
- 6 days with 7-8 hours lectures and supervised practical training each day:
- Mo-Fr, Sep 17-21 and Mo, Sep 24, 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 SS2012.
- 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!)
For students on the waiting list: please wait with the TUMonline registration for offical acceptance to the course.
There is currently no more space available.
You can still write an email to put your name on a waiting list.
Note to avoid any confusion: This course takes place at the end of the summer semester 2012 during the semester break. It will also be credited to the summer semester 2012!
Start: Mo, Sep 17, 2012, 10:00h - Lab 02.05.014
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