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Fuzzy measures of pixel cluster compactness
conference contribution
posted on 2014-09-04, 00:00 authored by Gleb BeliakovGleb Beliakov, Gang LiGang Li, Huy Quan Vu, Tim WilkinTim WilkinPixel-scale fine details are often lost during image processing tasks such as image reduction and filtering. Block or region based algorithms typically rely on averaging functions to implement the required operation and traditional function choices struggle to preserve small, spatially cohesive clusters of pixels which may be corrupted by noise. This article proposes the construction of fuzzy measures of cluster compactness to account for the spatial organisation of pixels. We present two construction methods (minimum spannning trees and fuzzy measure decomposition) to generate measures with specific properties: monotonicity with respect to cluster size; invariance with respect to translation, reflection and rotation; and, discrimination between pixel sets of fixed cardinality with different spatial arrangements. We apply these measures within a non-monotonic mode-like averaging function used for image reduction and we show that this new function preserves pixel-scale structures better than existing monotonie averages.
History
Event
IEEE International Conference on Fuzzy Systems (2014: Beijing, China)Pagination
1104 - 1111Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-06End date
2014-07-11ISSN
1098-7584ISBN-13
9781479920723Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEETitle of proceedings
FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy SystemsUsage metrics
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