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dc.contributor.authorMartinez-Ortiz, C
dc.contributor.authorEverson, R
dc.date.accessioned2016-03-30T13:28:48Z
dc.date.issued2013-10-31
dc.description.abstractMany compactness measures are available in the literature. In this paper we present a generalised compactness measure Cq(S) which unifies previously existing definitions of compactness. The new measure is based on Minkowski distances and incorporates a parameter q which modifies the behaviour of the compactness measure. Different shapes are considered to be most compact depending on the value of q: for q = 2, the most compact shape in 2D (3D) is a circle (a sphere); for q → ∞, the most compact shape is a square (a cube); and for q = 1, the most compact shape is a square (a octahedron). For a given shape S, measure Cq(S) can be understood as a function of q and as such it is possible to calculate a spectum of Cq(S) for a range of q. This produces a particular compactness signature for the shape S, which provides additional shape information. The experiments section of this paper provides illustrative examples where measure Cq(S) is applied to various shapes and describes how measure and its spectrum can be used for image processing applications.en_GB
dc.identifier.citationIEEE 13th UK Workshop on Computational Intelligence (UKCI), 9-11 September 2013, Guildford, UK, pp. 62 - 66en_GB
dc.identifier.doi10.1109/UKCI.2013.6651288
dc.identifier.urihttp://hdl.handle.net/10871/20874
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.subjectshape compactnessen_GB
dc.subjectshape descriptionen_GB
dc.subjectimage processingen_GB
dc.subjectcomputer visionen_GB
dc.titleMinkowski compactness measureen_GB
dc.typeConference paperen_GB
dc.date.available2016-03-30T13:28:48Z
dc.identifier.isbn978-1-4799-1566-8
dc.descriptionThis is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.en_GB


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