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dc.contributor.authorLe Van, Linh
dc.contributor.authorBeurton-Aimar, Marie
dc.contributor.authorKrahenbuhl, Adrien
dc.contributor.authorParisey, Nicolas
dc.contributor.editorSkala, Václav
dc.date.accessioned2018-05-21T08:43:52Z-
dc.date.available2018-05-21T08:43:52Z-
dc.date.issued2017
dc.identifier.citationWSCG '2017: short communications proceedings: The 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 29 - June 2 2017, p. 153-158.en
dc.identifier.isbn978-80-86943-45-9
dc.identifier.issn2464-4617
dc.identifier.uriwscg.zcu.cz/WSCG2017/!!_CSRN-2702.pdf
dc.identifier.urihttp://hdl.handle.net/11025/29746
dc.description.abstractIn biology, the morphometric analysis is widely used to analyze the inter-organisms variations. It allows to classify and to determine the evolution of an organism’s family. The morphometric methods consider features such as shape, structure, color, or size of the studied objects. In previous works [8], we have analyzed beetle mandibles by using the centroid as feature, in order to classify the beetles. We have shown that the Probabilistic Hough Transform (PHT) is an efficient unsupervised method to compute the centroid. This paper proposes a new approach to precisely estimate the landmark geometry, points of interest defined by biologists on the mandible contours. In order to automatically register the landmarks on different mandibles, we defined patches around manual landmarks of the reference image. Each patch is described by computing its SIFT descriptor. Considering a query image, we apply a registration step performed by an Iterative Principal Component Analysis which identify the rotation and translation parameters. Then, the patches in the query image are identified and the SIFT descriptors computed. The biologists have collected 293 beetles to provide two sets of mandible images separated into left and right side. The experiments show that, depending on the position of the landmarks on the mandible contour, the performance can go up to 98% of good detection. The complete workflow is implemented in the MAELab framework, freely available as library on GitHub.en
dc.format6 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.relation.ispartofseriesWSCG '2017: short communications proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectmorfologiecs
dc.subjectregistrace obrázkůcs
dc.subjectSIFT deskriptorcs
dc.subjectbroukcs
dc.subjectmandibulacs
dc.titleMAELab: a framework to automatize landmark estimationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedmorphologyen
dc.subject.translatedimage registrationen
dc.subject.translatedSIFT descriptoren
dc.subject.translatedbeetleen
dc.subject.translatedmandibleen
dc.type.statusPeer-revieweden
Appears in Collections:WSCG '2017: Short Papers Proceedings

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