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. Attached Files Name Description MIMEType Size Downloads Title Fuzzy measures of pixel cluster compactness Author(s) Beliakov,G orcid.org/0000-0002-9841-5292 Li,G orcid.org/0000-0003-1583-641X Vu,HQ 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 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 02 Feb 2015, 12:20:49 EST Mon, 02 Feb 2015, 12:22:03 EST Mon, 02 Feb 2015, 13:08:02 EST Mon, 02 Feb 2015, 13:15:11 EST Thu, 12 Feb 2015, 13:31:13 EST Thu, 12 Feb 2015, 13:32:07 EST Tue, 02 Jun 2015, 12:52:37 EST Wed, 14 Oct 2015, 14:12:58 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 538 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 02 Feb 2015, 12:22:03 EST

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