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Rapid Online Analysis of Local Feature Detectors and Their Complementarity

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

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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

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