Fuzzy binary patterns for uncertainty-aware texture representation
(University of Athens. Deptartament of Informatics and Telecommunications)Iakovidis, Dimitris
(Technological Educational Institute of Lamia. Deptartament of Informatics and Computer Technology)Maroulis, Dimitris
(University of Athens. Deptartament of Informatics and Telecommunications)
||The Local Binary Pattern (LBP) representation of textures has been proved useful for a wide range of pattern recognition applications, including texture segmentation, face detection, and biomedical image analysis. The interest of the research community in the LBP texture representation gave rise to plenty of LBP and other binary pattern (BP)-based variations. However, noise sensitivity is still a major concern to their applicability on the analysis of real world images. To cope with this problem we propose a generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models. The proposed methodology assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values. The texture discrimination capability of four representative FBP-based approaches has been evaluated on the basis of comprehensive classification experiments on three reference datasets of natural textures under various types and levels of additive noise. The results reveal that the FBP-based approaches lead to consistent improvement in texture classification as compared with the original BP-based approaches for various degrees of uncertainty. This improved performance is also validated by illustrative unsupervised segmentation experiments on natural scenes.
||Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
||article ; publishedVersion
Fuzzy Sets ;
||ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 10, Núm. 1 (December 2011) , p. 63-78, ISSN 1577-5097
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Record created 2012-02-15, last modified 2012-05-11