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The issue of noise is discussed and more robust descriptor based on Rapid-transform is introduced under noise condition.
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It is expected that by using more robust descriptors, better results can be achieved.
Hence, we believe both, more robust descriptors and proper number of sets for each region, can boost the performance of this framework.
It was shown that the expression group profiles are more robust descriptors than individual genes [ 39].
In good accordance with the original concept of "modularity" of biological functions systems [ 15], functional entities seem to be more robust descriptors than gene lists [ 16, 17].
Moreover, the more robust the descriptor, the better the results achieved with our proposal.
As a solution we propose a more robust structure descriptor based upon principal components analysis [ Pea01].
We also propose a more robust structure descriptor based on principal components analysis that better identifies anisotropic detail at the sub-kernel level.
By substituting more robust "shape descriptors" into these schemes we extend their applicability to structures formed from more complex building blocks.
The latter group is the focus of this article; considering LBP-based descriptors where the thresholding and encoding schemes are modified to create more noise robust descriptors.
The aim of the first experiment is to show that the semantic pixel set (sps -based sps -basedmakestrategyly the original LBP but also other LBP-based descriptors, makesrobust anotstable under different variationlyof facial appearance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com