Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

We consider a nonparametric, two-dimensional regression model that describes observations of Radon transformed images, i.e., an inverse regression model. Reconstructions from deterministic fan beam design by a certain kind of kernel-type estimators are considered and their asymptotic properties are investigated. The problem discussed is related to medical imaging procedures such as computerized tomography (CT).
Original languageEnglish
Title of host publication11th International Conference of Numerical Analysis and Applied Mathematics 2013
Subtitle of host publicationICNAAM 2013
EditorsTheodore Simos, George Psihoyios, Ch. Tsitouras
PublisherAIP
Pages728-731
ISBN (Print)978-0-7354-1184-5
DOIs
Publication statusPublished - 2013
Event11th International Conference of Numerical Analysis and Applied Mathematics 2013 - Rodos Palace Hotel, Rhodes, Greece
Duration: 21 Sep 201327 Sep 2013
Conference number: 11
http://history.icnaam.org/icnaam_2013/index.htm

Publication series

NameAIP Conference Proceedings
PublisherAIP
Volume1558
ISSN (Print)0094-243X

Conference

Conference11th International Conference of Numerical Analysis and Applied Mathematics 2013
Abbreviated titleICNAAM 2013
CountryGreece
CityRhodes
Period21/09/1327/09/13
Internet address

Keywords

  • Inverse problems
  • Multivariate regression
  • Nonparametric regression
  • Radon transform

Cite this

Proksch, K. (2013). Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design. In T. Simos, G. Psihoyios, & C. Tsitouras (Eds.), 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013 (pp. 728-731). (AIP Conference Proceedings; Vol. 1558). AIP. https://doi.org/10.1063/1.4825596
Proksch, Katharina. / Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design. 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013. editor / Theodore Simos ; George Psihoyios ; Ch. Tsitouras. AIP, 2013. pp. 728-731 (AIP Conference Proceedings).
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title = "Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design",
abstract = "We consider a nonparametric, two-dimensional regression model that describes observations of Radon transformed images, i.e., an inverse regression model. Reconstructions from deterministic fan beam design by a certain kind of kernel-type estimators are considered and their asymptotic properties are investigated. The problem discussed is related to medical imaging procedures such as computerized tomography (CT).",
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Proksch, K 2013, Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design. in T Simos, G Psihoyios & C Tsitouras (eds), 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013. AIP Conference Proceedings, vol. 1558, AIP, pp. 728-731, 11th International Conference of Numerical Analysis and Applied Mathematics 2013, Rhodes, Greece, 21/09/13. https://doi.org/10.1063/1.4825596

Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design. / Proksch, Katharina.

11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013. ed. / Theodore Simos; George Psihoyios; Ch. Tsitouras. AIP, 2013. p. 728-731 (AIP Conference Proceedings; Vol. 1558).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Proksch K. Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design. In Simos T, Psihoyios G, Tsitouras C, editors, 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013. AIP. 2013. p. 728-731. (AIP Conference Proceedings). https://doi.org/10.1063/1.4825596