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Article: Point sampling with general noise spectrum

TitlePoint sampling with general noise spectrum
Authors
KeywordsPoint sampling
Noise spectrum
Adaptive sampling
Issue Date2012
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.org
Citation
ACM Transactions on Graphics, 2012, v. 31 n. 4, article no. 76, p. 76:1-76:11 How to Cite?
Abstract
Point samples with different spectral noise properties (often defined using color names such as white, blue, green, and red) are important for many science and engineering disciplines including computer graphics. While existing techniques can easily produce white and blue noise samples, relatively little is known for generating other noise patterns. In particular, no single algorithm is available to generate different noise patterns according to user-defined spectra. In this paper, we describe an algorithm for generating point samples that match a user-defined Fourier spectrum function. Such a spectrum function can be either obtained from a known sampling method, or completely constructed by the user. Our key idea is to convert the Fourier spectrum function into a differential distribution function that describes the samples' local spatial statistics; we then use a gradient descent solver to iteratively compute a sample set that matches the target differential distribution function. Our algorithm can be easily modified to achieve adaptive sampling, and we provide a GPU-based implementation. Finally, we present a variety of different sample patterns obtained using our algorithm, and demonstrate suitable applications.
DescriptionSIGGRAPH 2012 Conference Proceedings
Persistent Identifierhttp://hdl.handle.net/10722/165833
ISSN
2013 Impact Factor: 3.725
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhou, Yen_US
dc.contributor.authorHuang, Hen_US
dc.contributor.authorWei, LYen_US
dc.contributor.authorWang, Ren_US
dc.date.accessioned2012-09-20T08:24:21Z-
dc.date.available2012-09-20T08:24:21Z-
dc.date.issued2012en_US
dc.identifier.citationACM Transactions on Graphics, 2012, v. 31 n. 4, article no. 76, p. 76:1-76:11en_US
dc.identifier.issn0730-0301-
dc.identifier.urihttp://hdl.handle.net/10722/165833-
dc.descriptionSIGGRAPH 2012 Conference Proceedings-
dc.description.abstractPoint samples with different spectral noise properties (often defined using color names such as white, blue, green, and red) are important for many science and engineering disciplines including computer graphics. While existing techniques can easily produce white and blue noise samples, relatively little is known for generating other noise patterns. In particular, no single algorithm is available to generate different noise patterns according to user-defined spectra. In this paper, we describe an algorithm for generating point samples that match a user-defined Fourier spectrum function. Such a spectrum function can be either obtained from a known sampling method, or completely constructed by the user. Our key idea is to convert the Fourier spectrum function into a differential distribution function that describes the samples' local spatial statistics; we then use a gradient descent solver to iteratively compute a sample set that matches the target differential distribution function. Our algorithm can be easily modified to achieve adaptive sampling, and we provide a GPU-based implementation. Finally, we present a variety of different sample patterns obtained using our algorithm, and demonstrate suitable applications.-
dc.languageengen_US
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.org-
dc.relation.ispartofACM Transactions on Graphicsen_US
dc.rightsACM Transactions on Graphics. Copyright © Association for Computing Machinery, Inc..-
dc.subjectPoint sampling-
dc.subjectNoise spectrum-
dc.subjectAdaptive sampling-
dc.titlePoint sampling with general noise spectrumen_US
dc.typeArticleen_US
dc.identifier.emailWei, LY: lywei@cs.hku.hken_US
dc.identifier.authorityWei, LY=rp01528en_US
dc.identifier.doi10.1145/2185520.2185572-
dc.identifier.hkuros206835en_US
dc.identifier.volume31en_US
dc.identifier.issue4-
dc.identifier.spage76:1en_US
dc.identifier.epage76:11en_US
dc.identifier.eissn1557-7368-
dc.identifier.isiWOS:000308250300052-
dc.publisher.placeUnited States-

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