Nonparametric regression based image analysis

P. A. Cornillon; N. Hengartner; E. Matzner-Løber; B. Thieurmel

doi:10.1007/978-1-4939-0569-0_17

Nonparametric regression based image analysis

P. A. Cornillon
, N. Hengartner
, E. Matzner-Løber
, B. Thieurmel

Université de Rennes 2
MST-8, Los Alamos National Laboratory
Data Knowledge

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

Multivariate nonparametric smoothers are adversely impacted by the sparseness of data in higher dimension, also known as the curse of dimensionality. Adaptive smoothers, that can exploit the underlying smoothness of the regression function, may partially mitigate this effect. We present an iterative procedure based on traditional kernel smoothers, thin plate spline smoothers or Duchon spline smoother that can be used when the number of covariates is important. However the method is limited to small sample sizes (n < 2,000) and we will propose some thoughts to circumvent that problem using, for example, pre-clustering of the data. Applications considered here are image denoising.

langue originale	Anglais
titre	Topics in Nonparametric Statistics - Proceedings of the 1st Conference of the International Society for Nonparametric Statistics
rédacteurs en chef	Dimitris N. Politis, Michael G. Akritas, Soumendra N. Lahiri
Editeur	Springer New York LLC
Pages	185-195
Nombre de pages	11
ISBN (Electronique)	9781493905683
Les DOIs	https://doi.org/10.1007/978-1-4939-0569-0_17
état	Publié - 1 janv. 2014
Modification externe	Oui
Evénement	1st Conference of the International Society of Nonparametric Statistics, ISNPS 2012 - Chalkidiki, Grcce Durée: 15 juin 2012 → 19 juin 2012

Série de publications

Nom	Springer Proceedings in Mathematics and Statistics
Volume	74
ISSN (imprimé)	2194-1009
ISSN (Electronique)	2194-1017

Une conférence

Une conférence	1st Conference of the International Society of Nonparametric Statistics, ISNPS 2012
Pays/Territoire	Grcce
La ville	Chalkidiki
période	15/06/12 → 19/06/12

Accès au document

10.1007/978-1-4939-0569-0_17

Contient cette citation

Cornillon, P. A., Hengartner, N., Matzner-Løber, E., & Thieurmel, B. (2014). Nonparametric regression based image analysis. Dans D. N. Politis, M. G. Akritas, & S. N. Lahiri (eds.), Topics in Nonparametric Statistics - Proceedings of the 1st Conference of the International Society for Nonparametric Statistics (p. 185-195). (Springer Proceedings in Mathematics and Statistics; Vol 74). Springer New York LLC. https://doi.org/10.1007/978-1-4939-0569-0_17

Cornillon, P. A. ; Hengartner, N. ; Matzner-Løber, E. et al. / Nonparametric regression based image analysis. Topics in Nonparametric Statistics - Proceedings of the 1st Conference of the International Society for Nonparametric Statistics. Editeur / Dimitris N. Politis ; Michael G. Akritas ; Soumendra N. Lahiri. Springer New York LLC, 2014. p. 185-195 (Springer Proceedings in Mathematics and Statistics).

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title = "Nonparametric regression based image analysis",

abstract = "Multivariate nonparametric smoothers are adversely impacted by the sparseness of data in higher dimension, also known as the curse of dimensionality. Adaptive smoothers, that can exploit the underlying smoothness of the regression function, may partially mitigate this effect. We present an iterative procedure based on traditional kernel smoothers, thin plate spline smoothers or Duchon spline smoother that can be used when the number of covariates is important. However the method is limited to small sample sizes (n < 2,000) and we will propose some thoughts to circumvent that problem using, for example, pre-clustering of the data. Applications considered here are image denoising.",

keywords = "Duchon splines, Image sequence denoising, Iterative bias reduction, Kernel smoother",

author = "Cornillon, \{P. A.\} and N. Hengartner and E. Matzner-L{\o}ber and B. Thieurmel",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media New York 2014.; 1st Conference of the International Society of Nonparametric Statistics, ISNPS 2012 ; Conference date: 15-06-2012 Through 19-06-2012",

year = "2014",

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doi = "10.1007/978-1-4939-0569-0\_17",

language = "English",

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pages = "185--195",

editor = "Politis, \{Dimitris N.\} and Akritas, \{Michael G.\} and Lahiri, \{Soumendra N.\}",

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Cornillon, PA, Hengartner, N, Matzner-Løber, E & Thieurmel, B 2014, Nonparametric regression based image analysis. Dans DN Politis, MG Akritas & SN Lahiri (eds), Topics in Nonparametric Statistics - Proceedings of the 1st Conference of the International Society for Nonparametric Statistics. Springer Proceedings in Mathematics and Statistics, VOL. 74, Springer New York LLC, p. 185-195, 1st Conference of the International Society of Nonparametric Statistics, ISNPS 2012, Chalkidiki, Grcce, 15/06/12. https://doi.org/10.1007/978-1-4939-0569-0_17

Nonparametric regression based image analysis. / Cornillon, P. A.; Hengartner, N.; Matzner-Løber, E. et al.
Topics in Nonparametric Statistics - Proceedings of the 1st Conference of the International Society for Nonparametric Statistics. Ed. / Dimitris N. Politis; Michael G. Akritas; Soumendra N. Lahiri. Springer New York LLC, 2014. p. 185-195 (Springer Proceedings in Mathematics and Statistics; Vol 74).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Nonparametric regression based image analysis

AU - Cornillon, P. A.

AU - Hengartner, N.

AU - Matzner-Løber, E.

AU - Thieurmel, B.

N1 - Publisher Copyright: © Springer Science+Business Media New York 2014.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Multivariate nonparametric smoothers are adversely impacted by the sparseness of data in higher dimension, also known as the curse of dimensionality. Adaptive smoothers, that can exploit the underlying smoothness of the regression function, may partially mitigate this effect. We present an iterative procedure based on traditional kernel smoothers, thin plate spline smoothers or Duchon spline smoother that can be used when the number of covariates is important. However the method is limited to small sample sizes (n < 2,000) and we will propose some thoughts to circumvent that problem using, for example, pre-clustering of the data. Applications considered here are image denoising.

AB - Multivariate nonparametric smoothers are adversely impacted by the sparseness of data in higher dimension, also known as the curse of dimensionality. Adaptive smoothers, that can exploit the underlying smoothness of the regression function, may partially mitigate this effect. We present an iterative procedure based on traditional kernel smoothers, thin plate spline smoothers or Duchon spline smoother that can be used when the number of covariates is important. However the method is limited to small sample sizes (n < 2,000) and we will propose some thoughts to circumvent that problem using, for example, pre-clustering of the data. Applications considered here are image denoising.

KW - Duchon splines

KW - Image sequence denoising

KW - Iterative bias reduction

KW - Kernel smoother

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EP - 195

BT - Topics in Nonparametric Statistics - Proceedings of the 1st Conference of the International Society for Nonparametric Statistics

A2 - Politis, Dimitris N.

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PB - Springer New York LLC

T2 - 1st Conference of the International Society of Nonparametric Statistics, ISNPS 2012

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Cornillon PA, Hengartner N, Matzner-Løber E, Thieurmel B. Nonparametric regression based image analysis. Dans Politis DN, Akritas MG, Lahiri SN, rédacteurs en chef, Topics in Nonparametric Statistics - Proceedings of the 1st Conference of the International Society for Nonparametric Statistics. Springer New York LLC. 2014. p. 185-195. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-1-4939-0569-0_17

Nonparametric regression based image analysis

Résumé

Série de publications

Une conférence

Accès au document

Empreinte digitale

Contient cette citation