文献
J-GLOBAL ID:201702232345096649
整理番号:17A1772341
データ異常値のためのロバストなStudentのt関数に基づく新しいトモグラフィー再構成法【Powered by NICT】
A Novel Tomographic Reconstruction Method Based on the Robust Student’s t Function For Suppressing Data Outliers
著者 (7件):
Kazantsev Daniil
(Manchester X-ray Imaging Facility, School of Materials, University of Manchester, Manchester, U.K.)
,
Bleichrodt Folkert
(Centrum Wiskunde & Informatica, Amsterdam, The Netherlands)
,
van Leeuwen Tristan
(Utrecht University, Utrecht, The Netherlands)
,
Kaestner Anders
(Neutron Imaging and Activation Group, Paul Scherrer Institute, Villigen, Switzerland)
,
Withers Philip J.
(Manchester X-ray Imaging Facility, School of Materials, University of Manchester, Manchester, U.K.)
,
Batenburg Kees Joost
(Centrum Wiskunde & Informatica, Amsterdam, The Netherlands)
,
Lee Peter D.
(Manchester X-ray Imaging Facility, School of Materials, University of Manchester, Manchester, U.K.)
資料名:
IEEE Transactions on Computational Imaging
(IEEE Transactions on Computational Imaging)
巻:
3
号:
4
ページ:
682-693
発行年:
2017年
JST資料番号:
W2428A
ISSN:
2333-9403
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)