poster

Evaluation of salient point methods

Authors:

Michael LewAuthors Info & Claims

MM '13: Proceedings of the 21st ACM international conference on Multimedia

Pages 685 - 688

https://doi.org/10.1145/2502081.2502179

Published: 21 October 2013 Publication History

Abstract

Processing visual content in images and videos is a challenging task associated with the development of modern computer vision. Because salient point approaches can represent distinctive and affine invariant points in images, many approaches have been proposed over the past decade. Each method has particular advantages and limitations and may be appropriate in different contexts. In this paper we evaluate the performance of a wide set of salient point detectors and descriptors. We begin by comparing diverse salient point algorithms (SIFT, SURF, BRIEF, ORB, FREAK, BRISK, STAR, GFTT and FAST) with regard to repeatability, recall and precision and then move to accuracy and stability in real-time video tracking.

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Cited By

Wu SOerlemans ABakker ELew M(2017)A comprehensive evaluation of local detectors and descriptorsSignal Processing: Image Communication10.1016/j.image.2017.06.01059(150-167)Online publication date: Nov-2017
https://doi.org/10.1016/j.image.2017.06.010
Fularz MKraft MSchmidt AKasiński A(2015)A High-Performance FPGA-Based Image Feature Detector and Matcher Based on the FAST and BRIEF AlgorithmsInternational Journal of Advanced Robotic Systems10.5772/6143412:10(141)Online publication date: 14-Oct-2015
https://doi.org/10.5772/61434

Index Terms

Evaluation of salient point methods
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
    1. Image manipulation

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '13: Proceedings of the 21st ACM international conference on Multimedia

October 2013

1166 pages

ISBN:9781450324045

DOI:10.1145/2502081

General Chairs:
Alejandro (Alex) Jaimes
Yahoo!, Spain
,
Nicu Sebe
University of Trento, Italy
,
Nozha Boujemaa
INRIA, France
,
Program Chairs:
Daniel Gatica-Perez
IDIAP & EPFL, Switzerland
,
David A. Shamma
Yahoo!, USA
,
Marcel Worring
University of Amsterdam, The Netherlands
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2013 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2013

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MM '13: ACM Multimedia Conference

October 21 - 25, 2013

Barcelona, Spain

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MM '13 Paper Acceptance Rate 47 of 235 submissions, 20%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Wu SOerlemans ABakker ELew M(2017)A comprehensive evaluation of local detectors and descriptorsSignal Processing: Image Communication10.1016/j.image.2017.06.01059(150-167)Online publication date: Nov-2017
https://doi.org/10.1016/j.image.2017.06.010
Fularz MKraft MSchmidt AKasiński A(2015)A High-Performance FPGA-Based Image Feature Detector and Matcher Based on the FAST and BRIEF AlgorithmsInternational Journal of Advanced Robotic Systems10.5772/6143412:10(141)Online publication date: 14-Oct-2015
https://doi.org/10.5772/61434

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