Supplementary data for the research on

Feature Point Detection in High Dynamic Range Imagery

Currently, two datasets exist, each of them as a supplementary material to one of our papers.

Evaluation of Feature Point Detection in High Dynamic Range Imagery

Dataset for download [ZIP, 9.6 GB] or for browsing. Date of last modification: 2015-12-15.

If you use the dataset, please, cite our paper:

PŘIBYL Bronislav, CHALMERS Alan, ZEMČÍK Pavel, HOOBERMAN Lucy and ČADÍK Martin. Evaluation of Feature Point Detection in High Dynamic Range Imagery. Journal of Visual Communication and Image Representation. Elsevier Science, 2016, vol. 38, pp. 141-160. ISSN 1047-3203.

Abstract:
This paper evaluates the suitability of High Dynamic Range (HDR) imaging techniques for Feature Point (FP) detection under demanding lighting conditions. The FPs are evaluated in HDR, tone mapped HDR, and traditional Low Dynamic Range (LDR) images. Eleven global and local tone mapping operators are evaluated and six widely used FP detectors are used in the experiments (Harris, Shi-Tomasi, DoG, Fast Hessian, FAST, and BRISK). The distribution and repeatability rate of FPs are studied under changes of camera viewpoint, camera distance, and scene lighting. The results of the experiments show that current FP detectors cannot cope with HDR images well. The best contemporary solution is thus tone mapping of HDR images using a local tone mapper as a pre-processing step.

Example images of image formats used in the experiments (the HDR and logHDR formats are not showed because they
cannot be reproduced on paper)

BibTex entry:

@ARTICLE{
   author = {Bronislav P{\v{r}}ibyl and Alan Chalmers and Pavel Zem{\v{c}}{\'{i}}k
                and Lucy Hooberman and Martin {\v{C}}ad{\'{i}}k},
   title = {Evaluation of Feature Point Detection in High Dynamic Range Imagery},
   pages = {141 - 160},
   journal = {Journal of Visual Communication and Image Representation},
   volume = {38},
   year = {2016},
   ISSN = {1047-3203},
   language = {english},
   doi = {10.1016/j.jvcir.2016.02.007},
   url = {http://dx.doi.org/10.1016/j.jvcir.2016.02.007}
}

Feature Point Detection under Extreme Lighting Conditions

Dataset for download [ZIP, 3.2 GB] or for browsing. Date of last modification: 2013-02-04.

If you use this dataset, please, cite our paper:

PŘIBYL Bronislav, CHALMERS Alan and ZEMČÍK Pavel. Feature Point Detection under Extreme Lighting Conditions. In: Spring Conference on Computer Graphics. Smolenice: Comenius University in Bratislava, 2012, pp. 156-163. ISBN 978-80-223-3211-8.

Abstract:
This paper evaluates the suitability of tone-mapped high dynamic range imagery 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 which image formats perform best and what the most appropriate scenarios for their use are.

Example images from sequences used in the experiments. A 2D scene (containing just one plane; top row) and a 3D scene(containing objects of different shapes; bottom row) were captured using four different image formats. These are, from left to right: HDR image tone mapped using a global tone mapper: GTM format (a, e). HDR image tone mapped using a local tone mapper: LTM format (b, f). Ordinary low dynamic range image: LDR format (c, g). LDR image filtered using Wallis filter: WAL format (d, h).

BibTex entry:

@INPROCEEDINGS{pribyl2012FPDuExLC,
   author = {Bronislav P{\v{r}}ibyl and Alan Chalmers
               and Pavel Zem{\v{c}}{\'{i}}k},
   title = {Feature Point Detection under Extreme Lighting Conditions},
   pages = {156--163},
   booktitle = {Spring Conference on Computer Graphics},
   year = {2012},
   location = {Smolenice, SK},
   publisher = {Comenius University in Bratislava},
   ISBN = {978-80-223-3211-8},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en?id=9920}
}

Author of this webpage: Bronislav Přibyl