UnivTrevesV1, PubMed, Corpus, bibRecord, 000409

Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records.

Identifieur interne : 000409 ( PubMed/Corpus ); précédent : 000408; suivant : 000410

Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records.

Auteurs : Florian Bernard ; Christian Eric Deuter ; Peter Gemmar ; Hartmut Schachinger

Source :

Computer methods and programs in biomedicine [ 1872-7565 ] ; 2013.

RBID : pubmed:23880079

English descriptors

KwdEn :
- Algorithms, Blinking (physiology), Electromyography, Eyelids (anatomy & histology), Humans, Image Processing, Computer-Assisted, Models, Biological, Psychophysiology (statistics & numerical data), Reflex, Startle (physiology), Video Recording.
MESH :
- anatomy & histology : Eyelids.
- physiology : Blinking, Reflex, Startle.
- statistics & numerical data : Psychophysiology.
- Algorithms, Electromyography, Humans, Image Processing, Computer-Assisted, Models, Biological, Video Recording.

Abstract

Using the positions of the eyelids is an effective and contact-free way for the measurement of startle induced eye-blinks, which plays an important role in human psychophysiological research. To the best of our knowledge, no methods for an efficient detection and tracking of the exact eyelid contours in image sequences captured at high-speed exist that are conveniently usable by psychophysiological researchers. In this publication a semi-automatic model-based eyelid contour detection and tracking algorithm for the analysis of high-speed video recordings from an eye tracker is presented. As a large number of images have been acquired prior to method development it was important that our technique is able to deal with images that are recorded without any special parametrisation of the eye tracker. The method entails pupil detection, specular reflection removal and makes use of dynamic model adaption. In a proof-of-concept study we could achieve a correct detection rate of 90.6%. With this approach, we provide a feasible method to accurately assess eye-blinks from high-speed video recordings.

DOI: 10.1016/j.cmpb.2013.06.003
PubMed: 23880079

Links to Exploration step

pubmed:23880079

Le document en format XML

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<author><name sortKey="Bernard, Florian" sort="Bernard, Florian" uniqKey="Bernard F" first="Florian" last="Bernard">Florian Bernard</name>
<affiliation><nlm:affiliation>Institute for Innovative Informatics Applications, Trier University of Applied Sciences, 54293 Trier, Germany. Electronic address: f.bernard@hochschule-trier.de.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Deuter, Christian Eric" sort="Deuter, Christian Eric" uniqKey="Deuter C" first="Christian Eric" last="Deuter">Christian Eric Deuter</name>
</author>
<author><name sortKey="Gemmar, Peter" sort="Gemmar, Peter" uniqKey="Gemmar P" first="Peter" last="Gemmar">Peter Gemmar</name>
</author>
<author><name sortKey="Schachinger, Hartmut" sort="Schachinger, Hartmut" uniqKey="Schachinger H" first="Hartmut" last="Schachinger">Hartmut Schachinger</name>
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<author><name sortKey="Bernard, Florian" sort="Bernard, Florian" uniqKey="Bernard F" first="Florian" last="Bernard">Florian Bernard</name>
<affiliation><nlm:affiliation>Institute for Innovative Informatics Applications, Trier University of Applied Sciences, 54293 Trier, Germany. Electronic address: f.bernard@hochschule-trier.de.</nlm:affiliation>
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<author><name sortKey="Deuter, Christian Eric" sort="Deuter, Christian Eric" uniqKey="Deuter C" first="Christian Eric" last="Deuter">Christian Eric Deuter</name>
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<author><name sortKey="Gemmar, Peter" sort="Gemmar, Peter" uniqKey="Gemmar P" first="Peter" last="Gemmar">Peter Gemmar</name>
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<author><name sortKey="Schachinger, Hartmut" sort="Schachinger, Hartmut" uniqKey="Schachinger H" first="Hartmut" last="Schachinger">Hartmut Schachinger</name>
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<term>Humans</term>
<term>Image Processing, Computer-Assisted</term>
<term>Models, Biological</term>
<term>Psychophysiology (statistics & numerical data)</term>
<term>Reflex, Startle (physiology)</term>
<term>Video Recording</term>
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<keywords scheme="MESH" qualifier="anatomy & histology" xml:lang="en"><term>Eyelids</term>
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<keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Blinking</term>
<term>Reflex, Startle</term>
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<front><div type="abstract" xml:lang="en">Using the positions of the eyelids is an effective and contact-free way for the measurement of startle induced eye-blinks, which plays an important role in human psychophysiological research. To the best of our knowledge, no methods for an efficient detection and tracking of the exact eyelid contours in image sequences captured at high-speed exist that are conveniently usable by psychophysiological researchers. In this publication a semi-automatic model-based eyelid contour detection and tracking algorithm for the analysis of high-speed video recordings from an eye tracker is presented. As a large number of images have been acquired prior to method development it was important that our technique is able to deal with images that are recorded without any special parametrisation of the eye tracker. The method entails pupil detection, specular reflection removal and makes use of dynamic model adaption. In a proof-of-concept study we could achieve a correct detection rate of 90.6%. With this approach, we provide a feasible method to accurately assess eye-blinks from high-speed video recordings.</div>
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<DateCreated><Year>2013</Year>
<Month>09</Month>
<Day>16</Day>
</DateCreated>
<DateCompleted><Year>2014</Year>
<Month>04</Month>
<Day>25</Day>
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<DateRevised><Year>2014</Year>
<Month>11</Month>
<Day>20</Day>
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<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1872-7565</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>112</Volume>
<Issue>1</Issue>
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<Title>Computer methods and programs in biomedicine</Title>
<ISOAbbreviation>Comput Methods Programs Biomed</ISOAbbreviation>
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<ArticleTitle>Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records.</ArticleTitle>
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<Abstract><AbstractText>Using the positions of the eyelids is an effective and contact-free way for the measurement of startle induced eye-blinks, which plays an important role in human psychophysiological research. To the best of our knowledge, no methods for an efficient detection and tracking of the exact eyelid contours in image sequences captured at high-speed exist that are conveniently usable by psychophysiological researchers. In this publication a semi-automatic model-based eyelid contour detection and tracking algorithm for the analysis of high-speed video recordings from an eye tracker is presented. As a large number of images have been acquired prior to method development it was important that our technique is able to deal with images that are recorded without any special parametrisation of the eye tracker. The method entails pupil detection, specular reflection removal and makes use of dynamic model adaption. In a proof-of-concept study we could achieve a correct detection rate of 90.6%. With this approach, we provide a feasible method to accurately assess eye-blinks from high-speed video recordings.</AbstractText>
<CopyrightInformation>Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.</CopyrightInformation>
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<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Bernard</LastName>
<ForeName>Florian</ForeName>
<Initials>F</Initials>
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<MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName>
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<MeshHeading><DescriptorName UI="D001767" MajorTopicYN="N">Blinking</DescriptorName>
<QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName>
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<MeshHeading><DescriptorName UI="D004576" MajorTopicYN="N">Electromyography</DescriptorName>
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<MeshHeading><DescriptorName UI="D005143" MajorTopicYN="N">Eyelids</DescriptorName>
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<QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014741" MajorTopicYN="N">Video Recording</DescriptorName>
</MeshHeading>
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<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Eye-blink detection</Keyword>
<Keyword MajorTopicYN="N">Eyelid detection</Keyword>
<Keyword MajorTopicYN="N">Eyelid tracking</Keyword>
<Keyword MajorTopicYN="N">Image processing</Keyword>
<Keyword MajorTopicYN="N">Segmentation</Keyword>
<Keyword MajorTopicYN="N">Startle eye-blink</Keyword>
</KeywordList>
</MedlineCitation>
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<Month>12</Month>
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<PubMedPubDate PubStatus="revised"><Year>2013</Year>
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Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records.

Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki