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Feature point detection under extreme lighting conditions

Authors:

Bronislav Přibyl,

Pavel ZemčíkAuthors Info & Claims

SCCG '12: Proceedings of the 28th Spring Conference on Computer Graphics

Pages 143 - 150

https://doi.org/10.1145/2448531.2448550

Published: 02 May 2012 Publication History

Abstract

This paper evaluates the suitability of High Dynamic Range (HDR) imaging techniques for feature point detection under extreme lighting conditions. The conditions are extreme in respect to the dynamic range of the lighting within the test scenes used. This dynamic range cannot be captured using standard low dynamic range imagery techniques without loss of detail. Four widely used feature point detectors are used in the experiments: Harris corner detector, Shi-Tomasi, FAST and Fast Hessian. Their repeatability rate is studied under changes of camera viewpoint, camera distance and scene lighting with respect to the image formats used. The results of the experiments show that HDR imaging techniques improve the repeatability rate of feature point detectors significantly.

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Cited By

Nascimento Ade Jesus Melo WDantas DAndrade B(2023)Feature point detection in HDR images based on coefficient of variationMultimedia Tools and Applications10.1007/s11042-023-16055-983:7(19981-20002)Online publication date: 29-Jul-2023
https://doi.org/10.1007/s11042-023-16055-9
Eşlik AAkarslan EHocaoğlu F(2022)Short-term solar radiation forecasting with a novel image processing-based deep learning approachRenewable Energy10.1016/j.renene.2022.10.063200(1490-1505)Online publication date: Nov-2022
https://doi.org/10.1016/j.renene.2022.10.063
Rani VKumar MSingh B(2022)Handcrafted Features for Human Gait Recognition: CASIA-A DatasetArtificial Intelligence and Data Science10.1007/978-3-031-21385-4_7(77-88)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-031-21385-4_7
Show More Cited By

Index Terms

Feature point detection under extreme lighting conditions
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation

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Published In

cover image ACM Other conferences

SCCG '12: Proceedings of the 28th Spring Conference on Computer Graphics

March 2013

158 pages

ISBN:9781450319775

DOI:10.1145/2448531

Conference Chair:
Carol O'Sullivan
Trinity College Dublin, Ireland

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Comenius University: Comenius University
SIS: Slovak informatics society

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2012

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Ministry of Education, Youth and Sports

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SCCG'12

Sponsor:

Comenius University
SIS

SCCG'12: Spring Conference on Computer Graphics

May 2 - 4, 2012

Budmerice, Slovakia

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Overall Acceptance Rate 67 of 115 submissions, 58%

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Cited By

Nascimento Ade Jesus Melo WDantas DAndrade B(2023)Feature point detection in HDR images based on coefficient of variationMultimedia Tools and Applications10.1007/s11042-023-16055-983:7(19981-20002)Online publication date: 29-Jul-2023
https://doi.org/10.1007/s11042-023-16055-9
Eşlik AAkarslan EHocaoğlu F(2022)Short-term solar radiation forecasting with a novel image processing-based deep learning approachRenewable Energy10.1016/j.renene.2022.10.063200(1490-1505)Online publication date: Nov-2022
https://doi.org/10.1016/j.renene.2022.10.063
Rani VKumar MSingh B(2022)Handcrafted Features for Human Gait Recognition: CASIA-A DatasetArtificial Intelligence and Data Science10.1007/978-3-031-21385-4_7(77-88)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-031-21385-4_7
Mstafa RYounis YHussein HAtto M(2020)A New Video Steganography Scheme Based on Shi-Tomasi Corner DetectorIEEE Access10.1109/ACCESS.2020.30213568(161825-161837)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3021356
Debattista K(2017)Application‐Specific Tone Mapping Via Genetic ProgrammingComputer Graphics Forum10.1111/cgf.1330737:1(439-450)Online publication date: Nov-2017
https://doi.org/10.1111/cgf.13307
Yamashita SZhang XMiyaki TRekimoto JCruz-Neira CKiyokawa KReiners DIwai DSteinicke F(2016)AquaCAVEProceedings of the 26th International Conference on Artificial Reality and Telexistence and the 21st Eurographics Symposium on Virtual Environments10.5555/3061323.3061329(25-28)Online publication date: 7-Oct-2016
https://dl.acm.org/doi/10.5555/3061323.3061329
Suma RStavropoulou GStathopoulou EVan Gool LGeorgopoulos AChalmers A(2016)Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applicationsVirtual Archaeology Review10.4995/var.2016.63197:15(54)Online publication date: 15-Nov-2016
https://doi.org/10.4995/var.2016.6319
Přibyl BChalmers AZemčík PHooberman Lźadík M(2016)Evaluation of feature point detection in high dynamic range imageryJournal of Visual Communication and Image Representation10.1016/j.jvcir.2016.02.00738:C(141-160)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jvcir.2016.02.007
Shewell CNugent CDonnelly MWang HEspinilla M(2016)Indoor localisation through object detection within multiple environments utilising a single wearable cameraHealth and Technology10.1007/s12553-016-0159-x7:1(51-60)Online publication date: 22-Dec-2016
https://doi.org/10.1007/s12553-016-0159-x
Chermak LAouf NRichardson M(2014)HDR imaging for feature tracking in challenging visibility scenesKybernetes10.1108/K-07-2014-013743:8(1129-1149)Online publication date: 26-Aug-2014
https://doi.org/10.1108/K-07-2014-0137

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