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Article: Differential domain analysis for non-uniform sampling
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TitleDifferential domain analysis for non-uniform sampling
 
AuthorsWei, LY2
Wang, R1
 
KeywordsAnalysis
Differential domain
Noise
Non-uniform
Sampling
Spectrum
 
Issue Date2011
 
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.org
 
CitationACM Transactions on Graphics, 2011, v. 30 n. 4, article no. 50, p. 50:1-50:10 [How to Cite?]
DOI: http://dx.doi.org/10.1145/1964921.1964945
 
AbstractSampling is a core component for many graphics applications including rendering, imaging, animation, and geometry processing. The efficacy of these applications often crucially depends upon the distribution quality of the underlying samples. While uniform sampling can be analyzed by using existing spatial and spectral methods, these cannot be easily extended to general non-uniform settings, such as adaptive, anisotropic, or non-Euclidean domains. We present new methods for analyzing non-uniform sample distributions. Our key insight is that standard Fourier analysis, which depends on samples' spatial locations, can be reformulated into an equivalent form that depends only on the distribution of their location differentials. We call this differential domain analysis. The main benefit of this reformulation is that it bridges the fundamental connection between the samples' spatial statistics and their spectral properties. In addition, it allows us to generalize our method with different computation kernels and differential measurements. Using this analysis, we can quantitatively measure the spatial and spectral properties of various non-uniform sample distributions, including adaptive, anisotropic, and non-Euclidean domains. © 2011 ACM.
 
DescriptionProceedings of ACM SIGGRAPH '11 Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, 7-11 August 2011
 
ISSN0730-0301
2013 Impact Factor: 3.725
 
DOIhttp://dx.doi.org/10.1145/1964921.1964945
 
ISI Accession Number IDWOS:000297216400024
Funding AgencyGrant Number
NSFCCF-0746577
Funding Information:

We would like to thank Hongwei Li for clarifying details in [Li et al. 2010], and SIGGRAPH anonymous reviewers for their suggestions. This work is supported in part by NSF grant CCF-0746577.

 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorWei, LY
 
dc.contributor.authorWang, R
 
dc.date.accessioned2011-09-27T03:02:15Z
 
dc.date.available2011-09-27T03:02:15Z
 
dc.date.issued2011
 
dc.description.abstractSampling is a core component for many graphics applications including rendering, imaging, animation, and geometry processing. The efficacy of these applications often crucially depends upon the distribution quality of the underlying samples. While uniform sampling can be analyzed by using existing spatial and spectral methods, these cannot be easily extended to general non-uniform settings, such as adaptive, anisotropic, or non-Euclidean domains. We present new methods for analyzing non-uniform sample distributions. Our key insight is that standard Fourier analysis, which depends on samples' spatial locations, can be reformulated into an equivalent form that depends only on the distribution of their location differentials. We call this differential domain analysis. The main benefit of this reformulation is that it bridges the fundamental connection between the samples' spatial statistics and their spectral properties. In addition, it allows us to generalize our method with different computation kernels and differential measurements. Using this analysis, we can quantitatively measure the spatial and spectral properties of various non-uniform sample distributions, including adaptive, anisotropic, and non-Euclidean domains. © 2011 ACM.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.descriptionProceedings of ACM SIGGRAPH '11 Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, 7-11 August 2011
 
dc.identifier.citationACM Transactions on Graphics, 2011, v. 30 n. 4, article no. 50, p. 50:1-50:10 [How to Cite?]
DOI: http://dx.doi.org/10.1145/1964921.1964945
 
dc.identifier.doihttp://dx.doi.org/10.1145/1964921.1964945
 
dc.identifier.eissn1557-7368
 
dc.identifier.hkuros206832
 
dc.identifier.isiWOS:000297216400024
Funding AgencyGrant Number
NSFCCF-0746577
Funding Information:

We would like to thank Hongwei Li for clarifying details in [Li et al. 2010], and SIGGRAPH anonymous reviewers for their suggestions. This work is supported in part by NSF grant CCF-0746577.

 
dc.identifier.issn0730-0301
2013 Impact Factor: 3.725
 
dc.identifier.issue4
 
dc.identifier.scopuseid_2-s2.0-80051890935
 
dc.identifier.urihttp://hdl.handle.net/10722/141807
 
dc.identifier.volume30
 
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.relation.referencesReferences in Scopus
 
dc.subjectAnalysis
 
dc.subjectDifferential domain
 
dc.subjectNoise
 
dc.subjectNon-uniform
 
dc.subjectSampling
 
dc.subjectSpectrum
 
dc.titleDifferential domain analysis for non-uniform sampling
 
dc.typeArticle
 
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Author Affiliations
  1. University of Massachusetts Amherst
  2. Microsoft Research