Automatic Selection of the Optimal Local Feature Detector
Automatic Selection of the Optimal Local Feature Detector
A large number of different local feature detectors have been proposed in the last few years. However, each feature detector has its own strengths and weaknesses that limit its use to a specific range of applications. In this paper is presented a tool capable of quickly analysing input images to determine which type and amount of transformation is applied to them and then selecting the optimal feature detector, which is expected to perform the best. The results show that the performance and the fast execution time render the proposed tool suitable for real-world vision applications.
Feature detector, Repeatability, Performance evaluation
284-289
Ferrarini, Bruno
a93ab204-5ccf-4b6d-a7c2-e02e65729924
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
Rehman, Naveed Ur
8cd2ee50-73fb-4df1-9bb5-b278b911b70f
Leonardis, Ales
ed38f4ad-444e-4850-9391-34aaf12ce8fd
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9
2016
Ferrarini, Bruno
a93ab204-5ccf-4b6d-a7c2-e02e65729924
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
Rehman, Naveed Ur
8cd2ee50-73fb-4df1-9bb5-b278b911b70f
Leonardis, Ales
ed38f4ad-444e-4850-9391-34aaf12ce8fd
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9
Ferrarini, Bruno, Ehsan, Shoaib, Rehman, Naveed Ur, Leonardis, Ales and McDonald-Maier, Klaus D.
(2016)
Automatic Selection of the Optimal Local Feature Detector.
In,
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2016).
(Lecture Notes in Computer Science, 9730)
.
(doi:10.1007/978-3-319-41501-7_32).
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Abstract
A large number of different local feature detectors have been proposed in the last few years. However, each feature detector has its own strengths and weaknesses that limit its use to a specific range of applications. In this paper is presented a tool capable of quickly analysing input images to determine which type and amount of transformation is applied to them and then selecting the optimal feature detector, which is expected to perform the best. The results show that the performance and the fast execution time render the proposed tool suitable for real-world vision applications.
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Published date: 2016
Keywords:
Feature detector, Repeatability, Performance evaluation
Identifiers
Local EPrints ID: 478952
URI: http://eprints.soton.ac.uk/id/eprint/478952
PURE UUID: 8dbf0eda-aee7-47fb-8aab-51df728adc29
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Date deposited: 14 Jul 2023 17:09
Last modified: 17 Mar 2024 04:16
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Contributors
Author:
Bruno Ferrarini
Author:
Shoaib Ehsan
Author:
Naveed Ur Rehman
Author:
Ales Leonardis
Author:
Klaus D. McDonald-Maier
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