Performances of invariant feature detectors in real-time video applications

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

3 Citations (Scopus)

Abstract

This paper reviews and compares the performance of five well-known detectors, SIFT, SURF, ORB, MSER and STAR, when combined in combination of with using three common descriptors, SIFT, SURF and ORB. To validate the results, these descriptors' performances are verified using three scenarios that differ with respect to changes in scale, light variation and rotation. The results show that the SIFT and SURF detectors possess the most stable features, with an overall accuracy of 80% under various conditions. Among the tested descriptors, SURF provides the best description of each keypoint.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages193-205
Number of pages13
Volume8237 LNCS
DOIs
Publication statusPublished - 2013
Event3rd International Visual Informatics Conference, IVIC 2013 - Selangor
Duration: 13 Nov 201315 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8237 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Visual Informatics Conference, IVIC 2013
CitySelangor
Period13/11/1315/11/13

Fingerprint

Scale Invariant Feature Transform
Descriptors
Detector
Detectors
Real-time
Invariant
Scenarios

Keywords

  • MSER
  • ORB
  • SIFT
  • SURF

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Hedayati, M., Wan Zaki, W. M. D., Zaki, W., Hussain, A., & Zulkifley, M. A. (2013). Performances of invariant feature detectors in real-time video applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8237 LNCS, pp. 193-205). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8237 LNCS). https://doi.org/10.1007/978-3-319-02958-0_19

Performances of invariant feature detectors in real-time video applications. / Hedayati, M.; Wan Zaki, Wan Mimi Diyana; Zaki, W.; Hussain, Aini; Zulkifley, Mohd Asyraf.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8237 LNCS 2013. p. 193-205 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8237 LNCS).

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

Hedayati, M, Wan Zaki, WMD, Zaki, W, Hussain, A & Zulkifley, MA 2013, Performances of invariant feature detectors in real-time video applications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8237 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8237 LNCS, pp. 193-205, 3rd International Visual Informatics Conference, IVIC 2013, Selangor, 13/11/13. https://doi.org/10.1007/978-3-319-02958-0_19
Hedayati M, Wan Zaki WMD, Zaki W, Hussain A, Zulkifley MA. Performances of invariant feature detectors in real-time video applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8237 LNCS. 2013. p. 193-205. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-02958-0_19
Hedayati, M. ; Wan Zaki, Wan Mimi Diyana ; Zaki, W. ; Hussain, Aini ; Zulkifley, Mohd Asyraf. / Performances of invariant feature detectors in real-time video applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8237 LNCS 2013. pp. 193-205 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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