Feature-point extraction based on an improved SIFT algorithm

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

8 Scopus citations

Abstract

SIFT (Scale Invariant Feature Transform) is an algorithm that extracts the feature data from an input image. It comprises robust characteristics that prevent image transformations such as the image size and rotation in the matching of feature points. However, it is disadvantageous because it is difficult to extract the feature points if the brightness distribution of the image or the image itself is concentrated in a specific range. In this paper, we propose an improved SIFT algorithm for disturbances such as sunflare by applying the CLAHE (Contrast Limited Adaptive Histogram Equalization), a histogram-equalization method, as a preprocessing SIFT method. For this paper, we implemented the algorithm using Visual Studio 2013, which can use the C ++ language, and implemented the histogram analyses in the MATLAB environment.

Original languageEnglish
Title of host publicationICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
PublisherIEEE Computer Society
Pages345-350
Number of pages6
ISBN (Electronic)9788993215137
DOIs
StatePublished - 13 Dec 2017
Event17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of
Duration: 18 Oct 201721 Oct 2017

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2017-October
ISSN (Print)1598-7833

Conference

Conference17th International Conference on Control, Automation and Systems, ICCAS 2017
Country/TerritoryKorea, Republic of
CityJeju
Period18/10/1721/10/17

Keywords

  • Feature-Point
  • Histogram Equalization
  • SIFT

Fingerprint

Dive into the research topics of 'Feature-point extraction based on an improved SIFT algorithm'. Together they form a unique fingerprint.

Cite this