期刊论文详细信息
ELCVIA Electronic Letters on Computer Vision and Image Analysis
FUZZY BINARY PATTERNS FOR UNCERTAINTY-AWARE TEXTURE REPRESENTATION
Eystratios Keramidas1  Dimitris Maroulis1  Dimitris Iakovidis2 
[1] Dept. of Informatics and Telecommunications, University of Athens;Dept. of Informatics and Computer Technology, Technological Educational Institute of Lamia
关键词: Statistical Pattern Recognition;    Feature Analysis;    Noise;    Fuzzy Sets;    Binary Patterns;   
DOI  :  
学科分类:计算机科学(综合)
来源: ELCVIA
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【 摘 要 】

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.

【 授权许可】

Unknown   

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