Parallelization of image processing and computer vision algorithms









The teaching areas are split in:

- regulated teaching, with Undergraduate courses (Bachelor of Engineering) including Bachelor Thesis, Graduate courses (Master of Science) including Master Thesis , PhD courses and Specialized Master courses.

- nonregulated teaching.

The teaching areas of degree and postgraduate courses covered by The Grupo de Tratamiento de Imágenes are the following:

- Digital Image Processing.

- Digital Television.

- Image Encoding.

- Multimedia System.

- Computer Vision.

- Graphics.

- Telecommunications Systems.

- Digital transmision.


Parallelization of image processing and computer vision algorithms  

Parallel processing in multi-processor systems 

Design and development of algorithms tailored to parallel architectures (multi-core and GPGPU):

- Uniprocessor performance is growing ever more slowly in the last decade.

- Video sources are growing in quality (and therefore size) faster than uniprocessor performance.-  

- Partial solution: multicore CPUs, but..

- Traditional sequential CPUs are not optimized for numeric computing throughput.

- Consumer-grade stream processors, aka GPUs, are designed for high throughput and energy efficiency in numeric computing.

- Data-parallel portions of computer vision algorithms can run 10x faster in these devices and help achieve real-time operation.

- Fast-evolving technology, still in its infancy.

- The programmer must take into account many architecture-specific details to get good performance.