2026-06-02T21:39:50Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/340632026-02-03T11:35:27Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Hidalgo-Paniagua, Alejandro
author
Vega-Rodríguez, Miguel A.
author
Pavón, Nieves
author
Ferruz, Joaquín
2024-09-30T12:28:24Z
2024-09-30T12:28:24Z
2013-10-11
https://hdl.handle.net/10630/34063
10.1002/cpe.3163
Nowadays, it is common to find problems that require recognizing objects in an image, tracking them along time, or recognizing a complete real-world scene. One of the most known and used algorithms to solve these problems is the Speeded Up Robust Features (SURF) algorithm. SURF is a fast and robust local, scale and rotation invariant, features detector. This means that it can be used for detecting and describing a set of points of interest (keypoints) from an image. Because of the importance of this algorithm and the rise of the parallelism-based technologies, in the last years, diverse parallel implementations of SURF have been proposed. These parallel implementations are based on very different techniques: Compute Unified Device Architecture, OpenMp, OpenCL, and so on. In conclusion, we think valuable a comparative study of all of them highlighting the advantages and disadvantages of each parallel implementation. To our best knowledge, this article is the first attempt to do this comparative study. In order to make this study, we have used the standard metrics and image collection in this field, as well as other important metrics in parallelism as speedup and efficiency.
eng
Soporte lógico
A comparative study of parallel software SURF implementations
journal article
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URL
https://riuma.uma.es/bitstreams/8dec7193-28b9-42c8-8b05-eb88bae8ee7c/download
File
MD5
6bd803df8a54652a83941499e1192cf4
397730
application/pdf
SURF.pdf