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Conference Paper: Differential domain analysis for non-uniform sampling
Title | Differential domain analysis for non-uniform sampling | ||||
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Authors | |||||
Keywords | Analysis Differential domain Noise Non-uniform Sampling Spectrum | ||||
Issue Date | 2011 | ||||
Publisher | Association for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.org | ||||
Citation | ACM SIGGRAPH '11 Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, 7-11 August 2011. In ACM Transactions on Graphics, 2011, v. 30 n. 4, article no. 50, p. 50:1-50:10 How to Cite? | ||||
Abstract | Sampling 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. | ||||
Persistent Identifier | http://hdl.handle.net/10722/141807 | ||||
ISSN | 2023 Impact Factor: 7.8 2023 SCImago Journal Rankings: 7.766 | ||||
ISI Accession Number ID |
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. | ||||
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wei, LY | en_HK |
dc.contributor.author | Wang, R | en_HK |
dc.date.accessioned | 2011-09-27T03:02:15Z | - |
dc.date.available | 2011-09-27T03:02:15Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.citation | ACM SIGGRAPH '11 Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, 7-11 August 2011. In ACM Transactions on Graphics, 2011, v. 30 n. 4, article no. 50, p. 50:1-50:10 | en_HK |
dc.identifier.issn | 0730-0301 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/141807 | - |
dc.description.abstract | Sampling 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. | en_HK |
dc.language | eng | en_US |
dc.publisher | Association for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.org | en_US |
dc.relation.ispartof | ACM Transactions on Graphics | en_HK |
dc.subject | Analysis | en_HK |
dc.subject | Differential domain | en_HK |
dc.subject | Noise | en_HK |
dc.subject | Non-uniform | en_HK |
dc.subject | Sampling | en_HK |
dc.subject | Spectrum | en_HK |
dc.title | Differential domain analysis for non-uniform sampling | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Wei, LY:lywei@cs.hku.hk | en_HK |
dc.identifier.authority | Wei, LY=rp01528 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1145/1964921.1964945 | en_HK |
dc.identifier.scopus | eid_2-s2.0-80051890935 | en_HK |
dc.identifier.hkuros | 206832 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-80051890935&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 30 | en_HK |
dc.identifier.issue | 4 | en_HK |
dc.identifier.eissn | 1557-7368 | - |
dc.identifier.isi | WOS:000297216400024 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Wei, LY=14523963300 | en_HK |
dc.identifier.scopusauthorid | Wang, R=36072127500 | en_HK |
dc.identifier.issnl | 0730-0301 | - |