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》 资源 》论文》CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching*

CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching*

2020-03-30 | |  99 |   40 |   0

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 intro- duce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational charac- teristics than other scale-space detectors, and are capable of real-time implementation.

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