In *Encyclopedia of Multimedia*, pages 484–485. Springer US, 2008. 00000

Paper abstract bibtex

Paper abstract bibtex

DefinitionMultichannel data ordering schemes are used for color image filtering and enhancements in order to produce better and more efficient results.Probably the most popular family of nonlinear filters is the one based on the concept of robust order-statistics [1–3]. In univariate (scalar) data analysis, it is sufficient to detect any outliers in the data in terms of their extremeness relative to an assumed basic model and then employ a robust accommodation method of inference. For multivariate data however, an additional step in the process is required, namely the adoption of the appropriate sub-ordering principle, such as marginal, conditional, partial, and reduced ordering as the basis for expressing extremeness of observations [1,2].Using marginal ordering the vector's components are ordered along each dimension independently. Since the marginal ordering approach often produces output vectors which differ from the set of vectorial inputs, application of marginal orderi ...

@incollection{furht_multichannel_2008, title = {Multichannel {Data} {Ordering} {Schemes}}, copyright = {©2008 Springer-Verlag}, isbn = {978-0-387-74724-8 978-0-387-78414-4}, url = {http://link.springer.com/referenceworkentry/10.1007/978-0-387-78414-4_123}, abstract = {DefinitionMultichannel data ordering schemes are used for color image filtering and enhancements in order to produce better and more efficient results.Probably the most popular family of nonlinear filters is the one based on the concept of robust order-statistics [1–3]. In univariate (scalar) data analysis, it is sufficient to detect any outliers in the data in terms of their extremeness relative to an assumed basic model and then employ a robust accommodation method of inference. For multivariate data however, an additional step in the process is required, namely the adoption of the appropriate sub-ordering principle, such as marginal, conditional, partial, and reduced ordering as the basis for expressing extremeness of observations [1,2].Using marginal ordering the vector's components are ordered along each dimension independently. Since the marginal ordering approach often produces output vectors which differ from the set of vectorial inputs, application of marginal orderi ...}, language = {en}, urldate = {2016-05-03}, booktitle = {Encyclopedia of {Multimedia}}, publisher = {Springer US}, editor = {Furht, Borko}, year = {2008}, note = {00000}, pages = {484--485} }

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