GPU-accelerated texture analysis using steerable Riesz wavelets

Joyseeree, Ranveer (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; et al.

Visual pattern recognition is a key research topic in the field of image processing and computer vision with many applications including medical diagnosis, identification and classification tasks. Texture analysis based on steerable Riesz wavelets is powerful, but requires computing pixel–wise operations resulting in a run time in the order of days when large volumes of data are processed. To overcome this limitation we propose a Graphics Processing Unit (GPU) based solution. A standard CPU version is used as starting point for the development of baseline GPU versions. To further increase the performance, and to overcome compute and memory limitations we apply a series of optimization techniques, leading to five versions in total. The best performing GPU solution ensures a speed–up of 93x for the parallelized section of the application and of 29.6x for the entire application. Furthermore, we show that a higher Riesz order and/or a higher image resolution further increases the speed–up.


Mots-clés:
Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG-VS
Institut:
Institut Informatique de gestion
Classification:
Informatique
Adresse bibliogr.:
Heraklion Crete, Greece, 17-19 February 2016
Date:
Heraklion Crete, Greece
17-19 February 2016
2016
Pagination:
6 p.
Publié dans
Proceedings of the 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) 2016
DOI:
ISSN:
2377-5750
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 Notice créée le 2016-10-04, modifiée le 2018-12-11

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