@InProceedings{FerreiraGastSchnNava:2020:PeErAn,
author = "Ferreira, Hermes H. and Gastal, Eduardo S. L. and Schnorr, Lucas
M. and Navaux, Philippe O. A.",
affiliation = "{Institute of Mathematics and Statistics - UFRGS} and {Institute
of Informatics - UFRGS} and {Institute of Informatics - UFRGS} and
{Institute of Informatics - UFRGS}",
title = "Performance and error analysis of recursive edge-aware Gaussian
filters on GPUs",
booktitle = "Proceedings...",
year = "2020",
editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and
Pelechano, Nuria and Wang, Zhangyang (Atlas)",
organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "edge-aware filtering, GPU processing, high-performance computing,
recursive filtering, image processing.",
abstract = "We present a schematic for image edge-aware Gaussian GPU filtering
which has linear complexity on the number of pixels of the image.
It allows us to reduce the execution time as we increase the
number of Streaming Multiprocessors (SMs) on the GPU. We make use
of a domain transformation and use a complex-valued recursive
formulation of the Gaussian filter. The algorithm partitions the
image in disjoint regions, where we compute in parallel the
filtering operations, avoiding communication between regions. Our
implementation leads to a real-time solution using a modern GPU.
With the RTX 2080 Ti, we achieved an execution time of less than
10 milliseconds for 2 filtering iterations on high-resolution RGB
images of dimensions 2048x2048.",
conference-location = "Porto de Galinhas (virtual)",
conference-year = "7-10 Nov. 2020",
doi = "10.1109/SIBGRAPI51738.2020.00020",
url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00020",
language = "en",
ibi = "8JMKD3MGPEW34M/43BC4T5",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BC4T5",
targetfile = "Ferreira_et_al_2020_Performance_edge-aware_filters_GPUs.pdf",
urlaccessdate = "2025, Feb. 16"
}