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Performance characterization of image feature detectors in relation to the scene content utilizing a large image database

Ferrarini, Bruno and Ehsan, Shoaib and Rehman, Naveed Ur and McDonald-Maier, Klaus D (2015) Performance characterization of image feature detectors in relation to the scene content utilizing a large image database. In: 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), 2015-09-10 - 2015-09-12.

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Abstract

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research. No state-of-the-art image feature detector works satisfactorily under all types of image transformations. Although the literature offers a variety of comparison works focusing on performance evaluation of image feature detectors under several types of image transformation, the influence of the scene content on the performance of local feature detectors has received little attention so far. This paper aims to bridge this gap with a new framework for determining the type of scenes, which maximize and minimize the performance of detectors in terms of repeatability rate. Several state-of-the-art feature detectors have been assessed utilizing a large database of 12936 images generated by applying uniform light and blur changes to 539 scenes captured from the real world. The results obtained provide new insights into the behaviour of feature detectors.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Notes: IWSSIP 2015
Uncontrolled Keywords: Local Feature Detection; Evaluation Framework; Performance Analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 01 Jul 2016 15:12
Last Modified: 15 Jan 2022 00:24
URI: http://repository.essex.ac.uk/id/eprint/17129

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