Thumbnail galleries for procedural models

Procedural modeling allows for the generation of innumerable variations of models from a parameterized, conditional or stochastic rule set. Due to the ness, complexity and stochastic nature of rule sets, it is often very difficult to have an understanding of the diversity of models that a given rule...

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Veröffentlicht in:Computer graphics forum Jg. 33; H. 2; S. 361 - 370
Hauptverfasser: Lienhard, S., Specht, M., Neubert, B., Pauly, M., Müller, P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.05.2014
Schlagworte:
Analysis
and systems
Categories and Subject Descriptors (according to ACM CCS)
Clusters
Computer graphics
Galleries
I.3.3 [Computer Graphics]: Picture/Image Generation-Display algorithms
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems
I.5.3 [Pattern Recognition]: Clustering-Similarity measures
languages
Mathematical models
Sampling
State of the art
Stochasticity
Studies
Visual
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Procedural modeling allows for the generation of innumerable variations of models from a parameterized, conditional or stochastic rule set. Due to the ness, complexity and stochastic nature of rule sets, it is often very difficult to have an understanding of the diversity of models that a given rule set defines. We address this problem by presenting a novel system to automatically generate, cluster, rank, and select a series of representative thumbnail images out of a rule set. We introduce a set of ‘view attributes’ that can be used to measure the suitability of an image to represent a model, and allow for comparison of different models derived from the same rule set. To find the best thumbnails, we exploit these view attributes on images of models obtained by stochastically sampling the parameter space of the rule set. The resulting thumbnail gallery gives a representative visual impression of the procedural modeling potential of the rule set. Performance is discussed by means of a number of distinct examples and compared to state‐of‐the‐art approaches.
Bibliographie:istex:3B37CD8B9D57C36672D09F5CC16D927E28F02466
ark:/67375/WNG-FT3TP7BF-5
ArticleID:CGF12317
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ObjectType-Article-2
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12317