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Due to the availability of general purpose programming interfaces like CUDA, the immense speed of graphics cards can be put to work for a multitude of parallel tasks. Algorithms for the analysis of images mostly work independently on different regions of an image. These algorithms are therefore inherently parallel and can greatly profit from parallel hardware.

Speedup factors in the order of two magnitudes make it possible to process and extract information from huge datasets, for example the images of the ImageNet Large Scale Visual Recognition Challenge. When experimenting with learning algorithms, the experiment duration is drastically reduced.

In the Lab, we learn how to implement learning algorithms from the area of visual pattern recognition and accelerate them using the CUDA C++ extension. It will be split into two parts. In the first part, you will first aquire knowledge of CUDA by programming and accelerating simple algorithms using parallel programming. In the second part, we implement learning algorithms with the help of an existing CUDA library. The lab will have weekly meetings. We'll agree on a time slot for these meetings in the first session.

We also recommend you take part in the seminar on vision systems.

You can participate in both, the project group and the seminar, or only take one of them. You should definitely consider taking both courses if you intend to write a thesis with our group.

Prerequisites

• Programming skills in C/C++ and/or another object oriented language
• Knowledge in the area of artificial intelligence and machine learning would be helpful.