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.
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
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