Detection and separation of generic-shaped objects by fuzzy clustering

Ali, M. Ameer; Karmakar, Gour C. and Dooley, Laurence S. (2010). Detection and separation of generic-shaped objects by fuzzy clustering. International Journal of Intelligent Computing and Cybernetics, 3(3) pp. 365–390.



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.

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