A 7-round parallel hardware-saving accelerator for Gaussian and DoG pyramid construction part of SIFT

Jingbang Qiu, Tianci Huang, Takeshi Ikenaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

SIFT, short for Scale Invariant Feature Transform, is regarded as one of the most robust feature detection algorithms. The Gaussian and DoG Pyramid Construction part, functioning as computation basis and searching spaces for other parts, proves fatal to the system. In this paper, we present an FPGA-implementable hardware accelerator for this part. Stratified Gaussian Convolution scheme and 7-Round Parallel Computation scheme are introduced to reduce the hardware cost and improve process speed, meanwhile keeping high accuracy. In our experiment, our proposal successfully realizes a system with max clock frequency of 95.0 MHz, and on-system process speed of up to 21 fps for VGA format images. Hardware cost of Slice LUTs is reduced by 12.1% compared with traditional work. Accuracy is kept as high as 98.27% against original software solution. Our proposed structure proves to be suitable for real-time SIFT systems.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Pages75-84
Number of pages10
EditionPART 3
DOIs
Publication statusPublished - 2010 Dec 29
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: 2009 Sep 232009 Sep 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume5996 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Asian Conference on Computer Vision, ACCV 2009
CountryChina
CityXi'an
Period09/9/2309/9/27

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Qiu, J., Huang, T., & Ikenaga, T. (2010). A 7-round parallel hardware-saving accelerator for Gaussian and DoG pyramid construction part of SIFT. In Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers (PART 3 ed., pp. 75-84). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5996 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-12297-2_8