Bayes Forest

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Potapov, Ilya Järvenpää, Marko Åkerblom, Markku Raumonen, Pasi Kaasalainen, Mikko 2018-02-09T10:01:51Z 2018-02-09T10:01:51Z 2017
dc.identifier.citation Potapov , I , Järvenpää , M , Åkerblom , M , Raumonen , P & Kaasalainen , M 2017 , ' Bayes Forest : A data-intensive generator of morphological tree clones ' GigaScience , vol 6 , no. 10 , gix079 , pp. 1-13 . DOI: 10.1093/gigascience/gix079 en
dc.identifier.issn 2047-217X
dc.identifier.other PURE UUID: 9116f9ca-6957-4680-976d-177804957f28
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE LINK:
dc.identifier.other PURE FILEURL:
dc.description.abstract Detailed and realistic tree form generators have numerous applications in ecology and forestry. For example, the varying morphology of trees contributes differently to formation of landscapes, natural habitats of species, and eco-physiological characteristics of the biosphere. Here, we present an algorithm for generating morphological tree "clones" based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules. The algorithm is designed to produce tree forms, i.e., morphological clones, similar (and not identical) in respect to tree-level structure, but varying in fine-scale structural detail. Although we opted for certain choices in our algorithm, individual parts may vary depending on the application, making it a general adaptable pipeline. Namely, we showed that a specific multipurpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies by means of empirical distributions describing the geometrical and topological features of a tree. Finally, we developed a programmable interface to manipulate data required by the algorithm. Our algorithm can be used in a variety of applications for exploration of the morphological potential of the growth models (both theoretical and experimental), arising in all sectors of plant science research. en
dc.format.extent 1-13
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries GigaScience en
dc.relation.ispartofseries Volume 6, issue 10 en
dc.rights openAccess en
dc.subject.other Health Informatics en
dc.subject.other Computer Science Applications en
dc.subject.other 113 Computer and information sciences en
dc.title Bayes Forest en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Tampere University of Technology
dc.contributor.department Department of Computer Science
dc.subject.keyword Empirical distributions
dc.subject.keyword Large scale data
dc.subject.keyword Morphological clone
dc.subject.keyword Quantitative structure tree model
dc.subject.keyword Stochastic data driven model
dc.subject.keyword Terrestrial laser scanning
dc.subject.keyword Health Informatics
dc.subject.keyword Computer Science Applications
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201802091402
dc.identifier.doi 10.1093/gigascience/gix079
dc.type.version publishedVersion

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