CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching
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.
Cite
Text
Agrawal et al. "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88693-8_8Markdown
[Agrawal et al. "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/agrawal2008eccv-censure/) doi:10.1007/978-3-540-88693-8_8BibTeX
@inproceedings{agrawal2008eccv-censure,
title = {{CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching}},
author = {Agrawal, Motilal and Konolige, Kurt and Blas, Morten Rufus},
booktitle = {European Conference on Computer Vision},
year = {2008},
pages = {102-115},
doi = {10.1007/978-3-540-88693-8_8},
url = {https://mlanthology.org/eccv/2008/agrawal2008eccv-censure/}
}