Distance measure with computer vision and neural networks for underwater applications

Luis M. Aristizabal; Carlos A. Zuluaga

doi:10.1109/CCRA.2016.7811421

Distance measure with computer vision and neural networks for underwater applications

Research output: Chapter in Book/Conference proceeding › Conference and proceedings › peer-review

4 Scopus citations

Abstract

This document describes a distance measurement system for underwater applications with computer vision and an Artificial Neural Network (ANN). The developed system is installed in the observation class remotely operated vehicle (ROV) Visor3, to be used in experimental testing. An integrated camera with two laser pointers are used to obtain input data to a Multi-Layer Perceptron (MLP) ANN, trained with a resilient backpropagation algorithm (RPROP). The solution described in this paper is an alternative to other methods found in the literature. Experimental results are presented.

Original language	English
Title of host publication	2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings
Editors	Henry Carrillo Lindado
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509037872
DOIs	https://doi.org/10.1109/CCRA.2016.7811421
State	Published - 9 Jan 2017
Event	1st IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Bogota D.C., Colombia Duration: 29 Sep 2016 → 30 Sep 2016

Publication series

Name	2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings

Conference

Conference	1st IEEE Colombian Conference on Robotics and Automation, CCRA 2016
Country/Territory	Colombia
City	Bogota D.C.
Period	29/09/16 → 30/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/CCRA.2016.7811421

Cite this

Aristizabal, L. M., & Zuluaga, C. A. (2017). Distance measure with computer vision and neural networks for underwater applications. In H. C. Lindado (Ed.), 2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings Article 7811421 (2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCRA.2016.7811421

Aristizabal, Luis M. ; Zuluaga, Carlos A. / Distance measure with computer vision and neural networks for underwater applications. 2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings. editor / Henry Carrillo Lindado. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings).

@inproceedings{904e70a7addc4b4d9d2018989f5c046e,

title = "Distance measure with computer vision and neural networks for underwater applications",

abstract = "This document describes a distance measurement system for underwater applications with computer vision and an Artificial Neural Network (ANN). The developed system is installed in the observation class remotely operated vehicle (ROV) Visor3, to be used in experimental testing. An integrated camera with two laser pointers are used to obtain input data to a Multi-Layer Perceptron (MLP) ANN, trained with a resilient backpropagation algorithm (RPROP). The solution described in this paper is an alternative to other methods found in the literature. Experimental results are presented.",

author = "Aristizabal, \{Luis M.\} and Zuluaga, \{Carlos A.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 1st IEEE Colombian Conference on Robotics and Automation, CCRA 2016 ; Conference date: 29-09-2016 Through 30-09-2016",

year = "2017",

month = jan,

day = "9",

doi = "10.1109/CCRA.2016.7811421",

language = "Ingl{\'e}s",

series = "2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings",

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editor = "Lindado, \{Henry Carrillo\}",

booktitle = "2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings",

}

Aristizabal, LM & Zuluaga, CA 2017, Distance measure with computer vision and neural networks for underwater applications. in HC Lindado (ed.), 2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings., 7811421, 2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 1st IEEE Colombian Conference on Robotics and Automation, CCRA 2016, Bogota D.C., Colombia, 29/09/16. https://doi.org/10.1109/CCRA.2016.7811421

Distance measure with computer vision and neural networks for underwater applications. / Aristizabal, Luis M.; Zuluaga, Carlos A.
2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings. ed. / Henry Carrillo Lindado. Institute of Electrical and Electronics Engineers Inc., 2017. 7811421 (2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings).

Research output: Chapter in Book/Conference proceeding › Conference and proceedings › peer-review

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T1 - Distance measure with computer vision and neural networks for underwater applications

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AU - Zuluaga, Carlos A.

PY - 2017/1/9

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N2 - This document describes a distance measurement system for underwater applications with computer vision and an Artificial Neural Network (ANN). The developed system is installed in the observation class remotely operated vehicle (ROV) Visor3, to be used in experimental testing. An integrated camera with two laser pointers are used to obtain input data to a Multi-Layer Perceptron (MLP) ANN, trained with a resilient backpropagation algorithm (RPROP). The solution described in this paper is an alternative to other methods found in the literature. Experimental results are presented.

AB - This document describes a distance measurement system for underwater applications with computer vision and an Artificial Neural Network (ANN). The developed system is installed in the observation class remotely operated vehicle (ROV) Visor3, to be used in experimental testing. An integrated camera with two laser pointers are used to obtain input data to a Multi-Layer Perceptron (MLP) ANN, trained with a resilient backpropagation algorithm (RPROP). The solution described in this paper is an alternative to other methods found in the literature. Experimental results are presented.

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DO - 10.1109/CCRA.2016.7811421

M3 - Ponencia publicada en las memorias del evento con ISBN

AN - SCOPUS:85012013367

T3 - 2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings

BT - 2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings

A2 - Lindado, Henry Carrillo

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st IEEE Colombian Conference on Robotics and Automation, CCRA 2016

Y2 - 29 September 2016 through 30 September 2016

ER -

Aristizabal LM , Zuluaga CA. Distance measure with computer vision and neural networks for underwater applications. In Lindado HC, editor, 2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 7811421. (2016 IEEE Colombian Conference on Robotics and Automation, CCRA 2016 - Conference Proceedings). doi: 10.1109/CCRA.2016.7811421

Distance measure with computer vision and neural networks for underwater applications

Abstract

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