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Ali, M. Ameer; Karmakar, Gour C. and Dooley, Laurence S.
(2010).
DOI: https://doi.org/10.1108/17563781011066684
Abstract
Purpose - Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically-shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily-shaped objects.
Design/Methodology/Approach – With the aim of separating arbitrary shaped objects in an image, this paper presents a new detection and separation of generic shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling.
Findings - Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects.
Originality/Value - The proposed FKG algorithm can be highly used in the applications where object segmentation is necessary. Like this algorithm can be applied in MPEG-4 for real object segmentation that is already applied in synthetic object segmentation.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
