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A model-based approach to recovering the structure of a plant from images

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dc.contributor.authorWard, B.
dc.contributor.authorBastian, J.
dc.contributor.authorvan den Hengel, A.
dc.contributor.authorPooley, D.
dc.contributor.authorRajendra, B.
dc.contributor.authorBerger, B.
dc.contributor.authorTester, M.
dc.contributor.conference13th European Conference on Computer Vision Workshops (ECCV 2014) (6 Sep 2014 - 7 Sep 2014 : Zurich, Switzerland)
dc.contributor.editorAgapito, L.
dc.contributor.editorBronstein, M.
dc.contributor.editorRother, C.
dc.date.issued2015
dc.descriptionECCV 2014 Workshops, Part IV, LNCS 8928
dc.description.abstractWe present a method for recovering the structure of a plant directly from a small set of widely-spaced images for automated analysis of phenotype. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is composed of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, without manual intervention.
dc.description.statementofresponsibilityBen Ward, John Bastian, Anton van den Hengel, Daniel Pooley, Rajendra Bari, Bettina Berger, and Mark Tester
dc.identifier.citationLecture Notes in Artificial Intelligence, 2015 / Agapito, L., Bronstein, M., Rother, C. (ed./s), vol.8928, pp.215-230
dc.identifier.doi10.1007/978-3-319-16220-1_16
dc.identifier.isbn9783319162195
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidvan den Hengel, A. [0000-0003-3027-8364]
dc.identifier.orcidBerger, B. [0000-0003-1195-4478]
dc.identifier.urihttp://hdl.handle.net/2440/112657
dc.language.isoen
dc.publisherSpringer International Publishing
dc.publisher.placeSwitzerland
dc.relation.granthttp://purl.org/au-research/grants/arc/LP130100156
dc.relation.ispartofseriesLecture Notes in Computer Science; 8928
dc.rights© Springer International Publishing Switzerland 2015
dc.source.urihttps://www.springer.com/gp/book/9783319162195
dc.subjectPlant phenotyping; image processing; plant architecture
dc.titleA model-based approach to recovering the structure of a plant from images
dc.typeConference paper
pubs.publication-statusPublished

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