JBoost optimization of color detectors for autonomous underwater vehicle navigation
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Issue number | PART 2 |
Pages (from-to) | 155-162 |
Number of pages | 8 |
ISSN | 0302-9743 |
DOIs | |
Publication status | Published - 2011 |
Externally published | Yes |
Event | 14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011 - Seville, Spain Duration: 29 Aug 2011 → 31 Aug 2011 |
Conference
Conference | 14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011 |
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Country | Spain |
City | Seville |
Period | 29/08/2011 → 31/08/2011 |
Sponsor | Universidad de Sevilla, Vicerrectorado de Investigacion, Inst. Mat. Univ. Sevilla, A. Castro Brzezicki, Fund. Invest. Desarro. Tecnol. Inf. Andalucia, Ministerio de Ciencia e Innovacion, Consejeria Econ., Cienc. Innovacion Junta Andalucia |
- AUV, boosting, color, object detection, Stingray
Research areas
ID: 301831308