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Rapid Online Analysis of Local Feature Detectors and Their Complementarity
AbstractA 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.
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MDPI and ACS Style
Ehsan, S.; Clark, A.F.; McDonald-Maier, K.D. Rapid Online Analysis of Local Feature Detectors and Their Complementarity. Sensors 2013, 13, 10876-10907.View more citation formats
Ehsan S, Clark AF, McDonald-Maier KD. Rapid Online Analysis of Local Feature Detectors and Their Complementarity. Sensors. 2013; 13(8):10876-10907.Chicago/Turabian Style
Ehsan, Shoaib; Clark, Adrian F.; McDonald-Maier, Klaus D. 2013. "Rapid Online Analysis of Local Feature Detectors and Their Complementarity." Sensors 13, no. 8: 10876-10907.