Computing parallel speeded-up robust features (P-SURF) via POSIX threads

Nan Zhang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

13 Citations (Scopus)

Abstract

Speeded-Up Robust Features (SURF), an image local feature extracting and describing method, finds and describes point correspondences between images with different viewing conditions. Despite the fact that it has recently been developed, SURF has already successfully found its applications in the area of computer vision, and was reported to be more appealing than the earlier Scale-Invariant Feature Transform (SIFT) in terms of robustness and performance. This paper presents a multi-threaded algorithm and its implementation that computes the same SURF. The algorithm parallelises several stages of computations in the original, sequential design. The main benefit brought about is the acceleration in computing the descriptor. Tests have been performed to show that the parallel SURF (P-SURF) generally shortened the computation time by a factor of 2 to 6 than the original, sequential method when running on multi-core processors.

Original languageEnglish
Title of host publicationEmerging Intelligent Computing Technology and Applications - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings
Pages287-296
Number of pages10
DOIs
Publication statusPublished - 2009
Event5th International Conference on Intelligent Computing, ICIC 2009 - Ulsan, Korea, Republic of
Duration: 16 Sept 200919 Sept 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5754 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Intelligent Computing, ICIC 2009
Country/TerritoryKorea, Republic of
CityUlsan
Period16/09/0919/09/09

Keywords

  • Image processing
  • Local feature extraction
  • Machine intelligence
  • Multi-threaded computing
  • Parallel programming

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