Performance comparison of image feature detectors utilizing a large number of scenes
Performance comparison of image feature detectors utilizing a large number of scenes
The availability of a large number of local invariant feature detectors has rendered the task of evaluating them an important issue in vision research. However, the maximum number of scenes utilized for performance comparison has so far been relatively small. This paper presents an evaluation framework and results based on it utilizing a large number of scenes, providing insights into the performance of local feature detectors under varying JPEG compression ratio, blur, and uniform light changes.
evaluation framework, local feature detection, performance analysis, repeatability
Ferrarini, Bruno
a93ab204-5ccf-4b6d-a7c2-e02e65729924
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
Rehman, Naveed Ur
8cd2ee50-73fb-4df1-9bb5-b278b911b70f
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9
January 2016
Ferrarini, Bruno
a93ab204-5ccf-4b6d-a7c2-e02e65729924
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
Rehman, Naveed Ur
8cd2ee50-73fb-4df1-9bb5-b278b911b70f
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9
Ferrarini, Bruno, Ehsan, Shoaib, Rehman, Naveed Ur and McDonald-Maier, Klaus D.
(2016)
Performance comparison of image feature detectors utilizing a large number of scenes.
Journal of Electronic Imaging, 25 (1), [010501].
(doi:10.1117/1.JEI.25.1.010501).
Abstract
The availability of a large number of local invariant feature detectors has rendered the task of evaluating them an important issue in vision research. However, the maximum number of scenes utilized for performance comparison has so far been relatively small. This paper presents an evaluation framework and results based on it utilizing a large number of scenes, providing insights into the performance of local feature detectors under varying JPEG compression ratio, blur, and uniform light changes.
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Published date: January 2016
Keywords:
evaluation framework, local feature detection, performance analysis, repeatability
Identifiers
Local EPrints ID: 477759
URI: http://eprints.soton.ac.uk/id/eprint/477759
ISSN: 1017-9909
PURE UUID: 0cf2fc4c-fc56-4c10-9133-0730862a66df
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Date deposited: 14 Jun 2023 16:35
Last modified: 17 Mar 2024 04:16
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Contributors
Author:
Bruno Ferrarini
Author:
Shoaib Ehsan
Author:
Naveed Ur Rehman
Author:
Klaus D. McDonald-Maier
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