Fuzzy measures of pixel cluster compactness

Beliakov,G, Li,G, Vu,HQ and Wilkin,T 2014, Fuzzy measures of pixel cluster compactness, in FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1104-1111, doi: 10.1109/FUZZ-IEEE.2014.6891754.

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Title Fuzzy measures of pixel cluster compactness
Author(s) Beliakov,GORCID iD for Beliakov,G orcid.org/0000-0002-9841-5292
Li,GORCID iD for Li,G orcid.org/0000-0003-1583-641X
Wilkin,TORCID iD for Wilkin,T orcid.org/0000-0003-4059-1354
Conference name IEEE International Conference on Fuzzy Systems (2014: Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2014
Conference series IEEE International Conference on Fuzzy Systems
Start page 1104
End page 1111
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Pixel-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.
ISBN 9781479920723
ISSN 1098-7584
Language eng
DOI 10.1109/FUZZ-IEEE.2014.6891754
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069336

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