JBoost optimization of color detectors for autonomous underwater vehicle navigation
Research output: Contribution to journal › Conference article › Research › peer-review
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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
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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