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Implicitly Weighted Methods in Robust Image Analysis
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SYSNO ASEP 0379860 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Implicitly Weighted Methods in Robust Image Analysis Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID Zdroj.dok. Journal of Mathematical Imaging and Vision. - : Springer - ISSN 0924-9907
Roč. 44, č. 3 (2012), s. 449-462Poč.str. 14 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova robustness ; high breakdown point ; outlier detection ; robust correlation analysis ; template matching ; face recognition Vědní obor RIV BB - Aplikovaná statistika, operační výzkum CEP 1M06014 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000307772900016 EID SCOPUS 84866051470 DOI 10.1007/s10851-012-0337-z Anotace This paper is devoted to highly robust statistical methods with applications to image analysis. The methods of the paper exploit the idea of implicit weighting, which is inspired by the highly robust least weighted squares regression estimator. We use a correlation coefficient based on implicit weighting of individual pixels as a highly robust similarity measure between two images. The reweighted least weighted squares estimator is considered as an alternative regression estimator with a clear interpretation. We apply implicit weighting to dimension reduction by means of robust principal component analysis. Highly robust methods are exploited in tasks of face localization and face detection in a database of 2D images. In this context we investigate a method for outlier detection and a filter for image denoising based on implicit weighting. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2013
Počet záznamů: 1