CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching

Citation

Agrawal, M., Konolige, K., Blas, M.R. (2008). CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88693-8_8

Abstract

We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.

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