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2017-03-14Zeitschriftenartikel DOI: 10.3390/biomimetics2010003
A Note on the Depth-from-Defocus Mechanism of Jumping Spiders
dc.contributor.authorNolte, Aleke
dc.contributor.authorHennes, Daniel
dc.contributor.authorIzzo, Dario
dc.contributor.authorBlum, Christian
dc.contributor.authorHafner, Verena
dc.contributor.authorGheysens, Tom
dc.date.accessioned2019-08-05T09:47:18Z
dc.date.available2019-08-05T09:47:18Z
dc.date.issued2017-03-14none
dc.date.updated2019-07-29T17:43:00Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/21109
dc.description.abstractJumping spiders are capable of estimating the distance to their prey relying only on the information from one of their main eyes. Recently, it has been shown that jumping spiders perform this estimation based on image defocus cues. In order to gain insight into the mechanisms involved in this blur-to-distance mapping as performed by the spider and to judge whether inspirations can be drawn from spider vision for depth-from-defocus computer vision algorithms, we constructed a three-dimensional (3D) model of the anterior median eye of the Metaphidippus aeneolus, a well studied species of jumping spider. We were able to study images of the environment as the spider would see them and to measure the performances of a well known depth-from-defocus algorithm on this dataset. We found that the algorithm performs best when using images that are averaged over the considerable thickness of the spider’s receptor layers, thus pointing towards a possible functional role of the receptor thickness for the spider’s depth estimation capabilities.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdepth estimationeng
dc.subjectdepth-from-defocuseng
dc.subjectcomputer visioneng
dc.subjectspider visioneng
dc.subjectspider eyeeng
dc.subject.ddc570 Biologienone
dc.titleA Note on the Depth-from-Defocus Mechanism of Jumping Spidersnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/21109-6
dc.identifier.doi10.3390/biomimetics2010003none
dc.identifier.doihttp://dx.doi.org/10.18452/20353
dc.type.versionpublishedVersionnone
local.edoc.container-titleBiomimeticsnone
local.edoc.pages14none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameMDPInone
local.edoc.container-publisher-placeBaselnone
local.edoc.container-volume2none
local.edoc.container-issue1none
local.edoc.container-firstpage3/1none
local.edoc.container-lastpage3/14none
dc.description.versionPeer Reviewednone
dc.identifier.eissn2313-7673
local.edoc.affiliationNolte, Aleke; Bernstein Center for Computational Neuroscience Berlin, Unter den Linden 6, 10099 Berlin, Germany,none
local.edoc.affiliationHennes, Daniel; Robotics Innovation Center, German Research Center for Artificial Intelligence, 28359 Bremen, Germany,none
local.edoc.affiliationIzzo, Dario; Advanced Concepts Team, European Space Agency, 2200AG Noordwijk, The Netherlands,none
local.edoc.affiliationBlum, Christian; Adaptive Systems Group, Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany,none
local.edoc.affiliationHafner, Verena; Adaptive Systems Group, Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany,none
local.edoc.affiliationGheysens, Tom; Polymer Chemistry & Biomaterials Research Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4Bis, B-9000 Ghent, Belgium,none

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