@inproceedings{abf9381a1fa446ab995eabc6a367d9e9,
title = "A Comparison of Feature Detectors for Underwater Sonar Imagery",
abstract = "In this work we compare the performance of seven popular feature detection algorithms on a synthetic sonar image dataset. The dataset consists of a single mine-like object (MLO) superimposed on three different backgrounds: grass, sand ripple, and sand. We explore the performance of Harris, Shi-Tomasi, SIFT, SURF, STAR, FAST, and ORB on each of these backgrounds, and all the backgrounds at once by training an SVM classifier. Performance is evaluated with ROC curves by comparing the number of correctly identified features belonging to objects (True Positives) and the number of incorrectly identified features belonging to background noise (False Positives).",
keywords = "Feature detection, Sonar, Visual odometry",
author = "Peter Tueller and Ryan Kastner and Roee Diamant",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; OCEANS 2018 MTS/IEEE Charleston, OCEANS 2018 ; Conference date: 22-10-2018 Through 25-10-2018",
year = "2019",
month = jan,
day = "7",
doi = "10.1109/OCEANS.2018.8604786",
language = "American English",
series = "OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018",
address = "United States",
}