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Thumbnail galleries for procedural models.

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Title: Thumbnail galleries for procedural models.
Authors: Lienhard, S.1,2, Specht, M.2, Neubert, B.1, Pauly, M.1, Müller, P.2
Source: Computer Graphics Forum. May2014, Vol. 33 Issue 2, p361-370. 10p. 6 Color Photographs, 2 Black and White Photographs, 4 Charts.
Subjects: Thumbnail images (Image processing), Image retrieval, Pixel density measurement, Menara Berkembar Petronas (Kuala Lumpur, Malaysia), Computational geometry, Pattern perception
Abstract: Procedural modeling allows for the generation of innumerable variations of models from a parameterized, conditional or stochastic rule set. Due to the abstractness, 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. [ABSTRACT FROM AUTHOR]
Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22Computer+Graphics+Forum%22">Computer Graphics Forum</searchLink>. May2014, Vol. 33 Issue 2, p361-370. 10p. 6 Color Photographs, 2 Black and White Photographs, 4 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Thumbnail+images+%28Image+processing%29%22">Thumbnail images (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+retrieval%22">Image retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Pixel+density+measurement%22">Pixel density measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Menara+Berkembar+Petronas+%28Kuala+Lumpur%2C+Malaysia%29%22">Menara Berkembar Petronas (Kuala Lumpur, Malaysia)</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+geometry%22">Computational geometry</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink>
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  Data: Procedural modeling allows for the generation of innumerable variations of models from a parameterized, conditional or stochastic rule set. Due to the abstractness, 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/cgf.12317
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        Text: English
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      – SubjectFull: Pixel density measurement
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      – SubjectFull: Menara Berkembar Petronas (Kuala Lumpur, Malaysia)
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              Text: May2014
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