Research Repository

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

Ferrarini, B and Ehsan, S and Rehman, NU and McDonald-Maier, KD (2015) Performance characterization of image feature detectors in relation to the scene content utilizing a large image database. In: UNSPECIFIED, ? - ?.

Full text not available from this repository.

Abstract

© 2015 IEEE. 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: Published proceedings: 2015 22nd International Conference on Systems, Signals and Image Processing - Proceedings of IWSSIP 2015
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 01 Jul 2016 15:12
Last Modified: 22 Jan 2019 22:15
URI: http://repository.essex.ac.uk/id/eprint/17129

Actions (login required)

View Item View Item