A Comparison of Feature Detectors for Underwater Sonar Imagery

Peter Tueller; Ryan Kastner; Roee Diamant

doi:10.1109/OCEANS.2018.8604786

A Comparison of Feature Detectors for Underwater Sonar Imagery

Peter Tueller, Ryan Kastner, Roee Diamant

Department of Marine Technologies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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).

Original language	English
Title of host publication	OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538648148
DOIs	https://doi.org/10.1109/OCEANS.2018.8604786
State	Published - 7 Jan 2019
Event	OCEANS 2018 MTS/IEEE Charleston, OCEANS 2018 - Charleston, United States Duration: 22 Oct 2018 → 25 Oct 2018

Publication series

Name	OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018

Conference

Conference	OCEANS 2018 MTS/IEEE Charleston, OCEANS 2018
Country/Territory	United States
City	Charleston
Period	22/10/18 → 25/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Feature detection
Sonar
Visual odometry

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Oceanography

Access to Document

10.1109/OCEANS.2018.8604786

Cite this

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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 = "English",

series = "OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Tueller, P, Kastner, R & Diamant, R 2019, A Comparison of Feature Detectors for Underwater Sonar Imagery. in OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018., 8604786, OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018, Institute of Electrical and Electronics Engineers Inc., OCEANS 2018 MTS/IEEE Charleston, OCEANS 2018, Charleston, United States, 22/10/18. https://doi.org/10.1109/OCEANS.2018.8604786

A Comparison of Feature Detectors for Underwater Sonar Imagery. / Tueller, Peter; Kastner, Ryan; Diamant, Roee.
OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018. Institute of Electrical and Electronics Engineers Inc., 2019. 8604786 (OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Comparison of Feature Detectors for Underwater Sonar Imagery

AU - Tueller, Peter

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AU - Diamant, Roee

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Y1 - 2019/1/7

N2 - 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).

AB - 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).

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KW - Visual odometry

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M3 - Conference contribution

AN - SCOPUS:85061814413

T3 - OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018

BT - OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - OCEANS 2018 MTS/IEEE Charleston, OCEANS 2018

Y2 - 22 October 2018 through 25 October 2018

ER -

A Comparison of Feature Detectors for Underwater Sonar Imagery

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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