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Automated quantification of the schooling behaviour of sticklebacks.

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Title: Automated quantification of the schooling behaviour of sticklebacks.
Authors: Ardekani, Reza1 dehestan@usc.edu, Greenwood, Anna K2 agreenwo@fhcrc.org, Peichel, Catherine L2 cpeichel@fhcrc.org, Tavaré, Simon1,3 stavare@usc.edu
Source: EURASIP Journal on Image & Video Processing. Dec2013, Vol. 2013 Issue 1, p1-8. 8p.
Subjects: Stickleback behavior, Fish schooling, Statistical methods in image analysis, Monitoring of fishes, Locus (Genetics), Groundfishes, Experimental design
Abstract: Sticklebacks have long been used as model organisms in behavioural biology. An important anti-predator behaviour in sticklebacks is schooling. We plan to use quantitative trait locus mapping to identify the genetic basis for differences in schooling behaviour between marine and benthic sticklebacks. To do this, we need to quantify the schooling behaviour of thousands of fish. We have developed a robust high-throughput video analysis method that allows us to screen a few thousand individuals automatically. We propose a non-local background modelling approach that allows us to detect and track sticklebacks and obtain the schooling parameters efficiently. [ABSTRACT FROM AUTHOR]
Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Automated quantification of the schooling behaviour of sticklebacks.
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  Data: <searchLink fieldCode="AR" term="%22Ardekani%2C+Reza%22">Ardekani, Reza</searchLink><relatesTo>1</relatesTo><i> dehestan@usc.edu</i><br /><searchLink fieldCode="AR" term="%22Greenwood%2C+Anna+K%22">Greenwood, Anna K</searchLink><relatesTo>2</relatesTo><i> agreenwo@fhcrc.org</i><br /><searchLink fieldCode="AR" term="%22Peichel%2C+Catherine+L%22">Peichel, Catherine L</searchLink><relatesTo>2</relatesTo><i> cpeichel@fhcrc.org</i><br /><searchLink fieldCode="AR" term="%22Tavaré%2C+Simon%22">Tavaré, Simon</searchLink><relatesTo>1,3</relatesTo><i> stavare@usc.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Stickleback+behavior%22">Stickleback behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Fish+schooling%22">Fish schooling</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+methods+in+image+analysis%22">Statistical methods in image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Monitoring+of+fishes%22">Monitoring of fishes</searchLink><br /><searchLink fieldCode="DE" term="%22Locus+%28Genetics%29%22">Locus (Genetics)</searchLink><br /><searchLink fieldCode="DE" term="%22Groundfishes%22">Groundfishes</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink>
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  Label: Abstract
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  Data: Sticklebacks have long been used as model organisms in behavioural biology. An important anti-predator behaviour in sticklebacks is schooling. We plan to use quantitative trait locus mapping to identify the genetic basis for differences in schooling behaviour between marine and benthic sticklebacks. To do this, we need to quantify the schooling behaviour of thousands of fish. We have developed a robust high-throughput video analysis method that allows us to screen a few thousand individuals automatically. We propose a non-local background modelling approach that allows us to detect and track sticklebacks and obtain the schooling parameters efficiently. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1186/1687-5281-2013-61
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      – Code: eng
        Text: English
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        PageCount: 8
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      – SubjectFull: Stickleback behavior
        Type: general
      – SubjectFull: Fish schooling
        Type: general
      – SubjectFull: Statistical methods in image analysis
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      – SubjectFull: Monitoring of fishes
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      – SubjectFull: Locus (Genetics)
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      – SubjectFull: Groundfishes
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      – SubjectFull: Experimental design
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      – TitleFull: Automated quantification of the schooling behaviour of sticklebacks.
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              Text: Dec2013
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