JBoost optimization of color detectors for autonomous underwater vehicle navigation

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

JBoost optimization of color detectors for autonomous underwater vehicle navigation. / Barngrover, Christopher; Belongie, Serge; Kastner, Ryan.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. PART 2, 2011, p. 155-162.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Barngrover, C, Belongie, S & Kastner, R 2011, 'JBoost optimization of color detectors for autonomous underwater vehicle navigation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, pp. 155-162. https://doi.org/10.1007/978-3-642-23678-5_17

APA

Barngrover, C., Belongie, S., & Kastner, R. (2011). JBoost optimization of color detectors for autonomous underwater vehicle navigation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (PART 2), 155-162. https://doi.org/10.1007/978-3-642-23678-5_17

Vancouver

Barngrover C, Belongie S, Kastner R. JBoost optimization of color detectors for autonomous underwater vehicle navigation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011;(PART 2):155-162. https://doi.org/10.1007/978-3-642-23678-5_17

Author

Barngrover, Christopher ; Belongie, Serge ; Kastner, Ryan. / JBoost optimization of color detectors for autonomous underwater vehicle navigation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011 ; No. PART 2. pp. 155-162.

Bibtex

@inproceedings{96c51fd7b0224b00bb7b5717c07cb53a,
title = "JBoost optimization of color detectors for autonomous underwater vehicle navigation",
abstract = "In the world of autonomous underwater vehicles (AUV) the prominent form of sensing is sonar due to cloudy water conditions and dispersion of light. Although underwater conditions are highly suitable for sonar, this does not mean that optical sensors should be completely ignored. There are situations where visibility is high, such as in calm waters, and where light dispersion is not significant, such as in shallow water or near the surface. In addition, even when visibility is low, once a certain proximity to an object exists, visibility can increase. The focus of this paper is this gap in capability for AUVs, with an emphasis on computer-aided detection through classifier optimization via machine learning. This paper describes the development of color-based classification algorithm and its application as a cost-sensitive alternative for navigation on the small Stingray AUV.",
keywords = "AUV, boosting, color, object detection, Stingray",
author = "Christopher Barngrover and Serge Belongie and Ryan Kastner",
year = "2011",
doi = "10.1007/978-3-642-23678-5_17",
language = "English",
pages = "155--162",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",
number = "PART 2",
note = "14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011 ; Conference date: 29-08-2011 Through 31-08-2011",

}

RIS

TY - GEN

T1 - JBoost optimization of color detectors for autonomous underwater vehicle navigation

AU - Barngrover, Christopher

AU - Belongie, Serge

AU - Kastner, Ryan

PY - 2011

Y1 - 2011

N2 - In the world of autonomous underwater vehicles (AUV) the prominent form of sensing is sonar due to cloudy water conditions and dispersion of light. Although underwater conditions are highly suitable for sonar, this does not mean that optical sensors should be completely ignored. There are situations where visibility is high, such as in calm waters, and where light dispersion is not significant, such as in shallow water or near the surface. In addition, even when visibility is low, once a certain proximity to an object exists, visibility can increase. The focus of this paper is this gap in capability for AUVs, with an emphasis on computer-aided detection through classifier optimization via machine learning. This paper describes the development of color-based classification algorithm and its application as a cost-sensitive alternative for navigation on the small Stingray AUV.

AB - In the world of autonomous underwater vehicles (AUV) the prominent form of sensing is sonar due to cloudy water conditions and dispersion of light. Although underwater conditions are highly suitable for sonar, this does not mean that optical sensors should be completely ignored. There are situations where visibility is high, such as in calm waters, and where light dispersion is not significant, such as in shallow water or near the surface. In addition, even when visibility is low, once a certain proximity to an object exists, visibility can increase. The focus of this paper is this gap in capability for AUVs, with an emphasis on computer-aided detection through classifier optimization via machine learning. This paper describes the development of color-based classification algorithm and its application as a cost-sensitive alternative for navigation on the small Stingray AUV.

KW - AUV

KW - boosting

KW - color

KW - object detection

KW - Stingray

UR - http://www.scopus.com/inward/record.url?scp=80052808192&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-23678-5_17

DO - 10.1007/978-3-642-23678-5_17

M3 - Conference article

AN - SCOPUS:80052808192

SP - 155

EP - 162

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

IS - PART 2

T2 - 14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011

Y2 - 29 August 2011 through 31 August 2011

ER -

ID: 301831308