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Article: Point sampling with general noise spectrum
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TitlePoint sampling with general noise spectrum
 
AuthorsZhou, Y
Huang, H
Wei, LY
Wang, R
 
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
 
CitationACM Transactions on Graphics, 2012, v. 31 n. 4, article no. 76, p. 76:1-76:11 [How to Cite?]
DOI: http://dx.doi.org/10.1145/2185520.2185572
 
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.
 
DescriptionSIGGRAPH 2012 Conference Proceedings
 
ISSN0730-0301
2013 Impact Factor: 3.725
 
DOIhttp://dx.doi.org/10.1145/2185520.2185572
 
DC FieldValue
dc.contributor.authorZhou, Y
 
dc.contributor.authorHuang, H
 
dc.contributor.authorWei, LY
 
dc.contributor.authorWang, R
 
dc.date.accessioned2012-09-20T08:24:21Z
 
dc.date.available2012-09-20T08:24:21Z
 
dc.date.issued2012
 
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.descriptionSIGGRAPH 2012 Conference Proceedings
 
dc.identifier.citationACM Transactions on Graphics, 2012, v. 31 n. 4, article no. 76, p. 76:1-76:11 [How to Cite?]
DOI: http://dx.doi.org/10.1145/2185520.2185572
 
dc.identifier.doihttp://dx.doi.org/10.1145/2185520.2185572
 
dc.identifier.eissn1557-7368
 
dc.identifier.epage76:11
 
dc.identifier.hkuros206835
 
dc.identifier.issn0730-0301
2013 Impact Factor: 3.725
 
dc.identifier.issue4
 
dc.identifier.spage76:1
 
dc.identifier.urihttp://hdl.handle.net/10722/165833
 
dc.identifier.volume31
 
dc.languageeng
 
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.org
 
dc.publisher.placeUnited States
 
dc.relation.ispartofACM Transactions on Graphics
 
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 spectrum
 
dc.typeArticle
 
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<contributor.author>Huang, H</contributor.author>
<contributor.author>Wei, LY</contributor.author>
<contributor.author>Wang, R</contributor.author>
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<description.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&apos; 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.</description.abstract>
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<title>Point sampling with general noise spectrum</title>
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