Abstract
This paper reviews and compares the performance of five well-known detectors, SIFT, SURF, ORB, MSER and STAR, when combined in combination of with using three common descriptors, SIFT, SURF and ORB. To validate the results, these descriptors’ performances are verified using three scenarios that differ with respect to changes in scale, light variation and rotation. The results show that the SIFT and SURF detectors possess the most stable features, with an overall accuracy of 80% under various conditions. Among the tested descriptors, SURF provides the best description of each keypoint.
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Hedayati, M., Diyana, W.M., Zaki, W., Hussain, A., Zulkifley, M.A. (2013). Performances of Invariant Feature Detectors in Real-Time Video Applications. In: Zaman, H.B., Robinson, P., Olivier, P., Shih, T.K., Velastin, S. (eds) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science, vol 8237. Springer, Cham. https://doi.org/10.1007/978-3-319-02958-0_19
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DOI: https://doi.org/10.1007/978-3-319-02958-0_19
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