Rapid Online Analysis of Local Feature Detectors and Their Complementarity

Sensors

Shoaib Ehsan¹  Adrian F. Clark² 

[1]School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK

关键词: local feature detection;    coverage;    complementarity;    combining feature detectors;    prediction-based framework;   

DOI  :  10.3390/s130810876

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.