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DC Field
Value
Language
dc.contributor.author
Harmening, Corinna
-
dc.contributor.author
Neuner, Hans-Berndt
-
dc.contributor.editor
Novák, Pavel
-
dc.contributor.editor
Crespi, Mattia
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dc.contributor.editor
Sneeuw, Nico
-
dc.contributor.editor
Sansò, Fernando
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dc.date.accessioned
2022-08-02T14:59:16Z
-
dc.date.available
2022-08-02T14:59:16Z
-
dc.date.issued
2020
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dc.identifier.citation
<div class="csl-bib-body">
<div class="csl-entry">Harmening, C., & Neuner, H.-B. (2020). Using structural risk minimization to determine the optimal complexity of B-spline surfaces for modelling correlated point cloud data. In P. Novák, M. Crespi, N. Sneeuw, & F. Sansò (Eds.), <i>IX Hotine-Marussi Symposium on Mathematical Geodesy. Proceedings of the Symposium in Rome, June 18-22, 2018</i> (pp. 165–174). International Association of Geodesy Symposia / Springer. http://hdl.handle.net/20.500.12708/44053</div>
</div>
-
dc.identifier.isbn
978-3-030-54266-5
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/44053
-
dc.description.abstract
The increased use of areal measurement techniques in engineering geodesy requires the development of adequate areal analysis strategies. Usually, such analysis strategies include a modelling of the data in order to reduce the amount of data while preserving as much information as possible. Free form surfaces like B-splines have been proven to be an appropriate tool to model point clouds. The complexity of those surfaces is among other model parameters determined by the number of control points. The selection of the appropriate number of control points constitutes a model selection task, which is typically solved under consideration of parsimony by trial-and-error procedures. In Harmening & Neuner (2016) and Harmening & Neuner (2017) a model selection approach based on structural risk minimization was developed for this specific problem. However, neither this strategy, nor standard model selection methods take correlations into account. For this reason, the performance of the developed model selection approach on correlated data sets is investigated and the respective results are compared to those provided by a standard model selection method, the Bayesian Information Criterion.
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
The increased use of areal measurement techniques in engineering geodesy requires the development of adequate areal analysis strategies. Usually, such analysis strategies include a modelling of the data in order to reduce the amount of data while preserving as much information as possible. Free form surfaces like B-splines have been proven to be an appropriate tool to model point clouds. The complexity of those surfaces is among other model parameters determined by the number of control points. The selection of the appropriate number of control points constitutes a model selection task, which is typically solved under consideration of parsimony by trial-and-error procedures. In Harmening & Neuner (2016) and Harmening & Neuner (2017) a model selection approach based on structural risk minimization was developed for this specific problem. However, neither this strategy, nor standard model selection methods take correlations into account. For this reason, the performance of the developed model selection approach on correlated data sets is investigated and the respective results are compared to those provided by a standard model selection method, the Bayesian Information Criterion.

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

en
dc.publisher
International Association of Geodesy Symposia / Springer
-
dc.subject
Model selection
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dc.subject
B-spline surfaces
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dc.subject
Correlated point clouds
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dc.subject
Point cloud modelling
-
dc.subject
Structural risk minimization
-
dc.subject
VC-dimension
-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject

-
dc.subject
&
-
dc.title
Using structural risk minimization to determine the optimal complexity of B-spline surfaces for modelling correlated point cloud data
-
dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.relation.publication
IX Hotine-Marussi Symposium on Mathematical Geodesy. Proceedings of the Symposium in Rome, June 18-22, 2018
-
dc.description.startpage
165
-
dc.description.endpage
174
-
dc.type.category
Full-Paper Contribution
-
dc.publisher.place
151
-
tuw.booktitle
IX Hotine-Marussi Symposium on Mathematical Geodesy. Proceedings of the Symposium in Rome, June 18-22, 2018
-
tuw.peerreviewed
true
-
tuw.researchTopic.id
C6
-
tuw.researchTopic.name
Modelling and Simulation
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E120-05 - Forschungsbereich Ingenieurgeodäsie
-
dc.description.numberOfPages
10
-
tuw.event.name
IX Hotine-Marussi Symposium on Mathematical Geodesy
-
tuw.event.startdate
18-06-2018
-
tuw.event.enddate
22-06-2018
-
tuw.event.online
On Site
-
tuw.event.place
Rom
-
tuw.event.place
Rom
-
tuw.event.country
EU
-
tuw.event.presenter
Harmening, Corinna
-
wb.sciencebranch
Geodäsie, Vermessungswesen
-
wb.sciencebranch.oefos
2074
-
wb.facultyfocus
Integrierte Geodäsie und Geodynamik
de
wb.facultyfocus
Integrated Geodesy and Geodynamics
en
wb.facultyfocus.faculty
E100
-
wb.presentation.type
science to science/art to art
-
item.fulltext
no Fulltext
-
item.cerifentitytype
Publications
-
item.openairecristype
http://purl.org/coar/resource_type/c_5794
-
item.openairetype
conference paper
-
item.grantfulltext
none
-
crisitem.author.dept
E120-05 - Forschungsbereich Ingenieurgeodäsie
-
crisitem.author.dept
E120-05 - Forschungsbereich Ingenieurgeodäsie
-
crisitem.author.parentorg
E120 - Department für Geodäsie und Geoinformation
-
crisitem.author.parentorg
E120 - Department für Geodäsie und Geoinformation
-
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