Ehsan, Shoaib and Clark, Adrian and McDonald-Maier, Klaus (2013) 'Rapid Online Analysis of Local Feature Detectors and Their Complementarity.' Sensors, 13 (8). pp. 10876-10907. ISSN 1424-8220
|
Text
sensors-13-10876.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
A vision system that can assess its own performance and take appropriate actions online to maximize its effectiveness would be a step towards achieving the long-cherished goal of imitating humans. This paper proposes a method for performing an online performance analysis of local feature detectors, the primary stage of many practical vision systems. It advocates the spatial distribution of local image features as a good performance indicator and presents a metric that can be calculated rapidly, concurs with human visual assessments and is complementary to existing offline measures such as repeatability. The metric is shown to provide a measure of complementarity for combinations of detectors, correctly reflecting the underlying principles of individual detectors. Qualitative results on well-established datasets for several state-of-the-art detectors are presented based on the proposed measure. Using a hypothesis testing approach and a newly-acquired, larger image database, statistically-significant performance differences are identified. Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features. A principled framework for combining feature detectors in these applications is also presented. Timing results reveal the potential of the metric for online applications. © 2013 by the authors; licensee MDPI, Basel, Switzerland.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | local feature detection; coverage; complementarity; combining feature detectors; prediction-based framework |
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: | 22 Aug 2013 08:50 |
Last Modified: | 15 Jan 2022 00:21 |
URI: | http://repository.essex.ac.uk/id/eprint/7403 |
Actions (login required)
![]() |
View Item |