Implicitly Weighted Methods in Robust Image Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F12%3A00379860" target="_blank" >RIV/67985807:_____/12:00379860 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10851-012-0337-z" target="_blank" >http://dx.doi.org/10.1007/s10851-012-0337-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10851-012-0337-z" target="_blank" >10.1007/s10851-012-0337-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Implicitly Weighted Methods in Robust Image Analysis

Popis výsledku v původním jazyce

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 acorrelation 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.

Název v anglickém jazyce

Implicitly Weighted Methods in Robust Image Analysis

Popis výsledku anglicky

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 acorrelation 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/1M06014" target="_blank" >1M06014: Centrum biomedicínské informatiky (CBI)</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Mathematical Imaging and Vision

ISSN

0924-9907

e-ISSN

—

Svazek periodika

44

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

449-462

Kód UT WoS článku

000307772900016

EID výsledku v databázi Scopus

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