Implicitly Weighted Methods in Robust Image Analysis

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Implicitly Weighted Methods in Robust Image Analysis
Original language description
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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Project
<a href="/en/project/1M06014" target="_blank" >1M06014: Centre of Biomedical Informatics</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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