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Title: Qualitative primitive identification using fuzzy clustering and invariant approach
Authors: Cai, Y.Y. 
Loh, H.T. 
Nee, A.Y.C. 
Keywords: Fitting and segmentation
Fuzzy partitioning
Issue Date: Jul-1996
Citation: Cai, Y.Y., Loh, H.T., Nee, A.Y.C. (1996-07). Qualitative primitive identification using fuzzy clustering and invariant approach. Image and Vision Computing 14 (7) : 451-464. ScholarBank@NUS Repository.
Abstract: This paper addresses the issue of qualitative primitive extraction from range images. The fuzzy C-shell clustering technique is applied to partition range images into a set of quadric shells. Once the best-fitting quadric shells have been recovered, five types of invariants are used to classify the qualitative shapes and represent the geometric parameters of the shapes. Finally, the extracted quadric shells are localised in terms of quadric centre(s), quadric directions, and any axes of revolution. Using fuzzy shell clustering, the shell features can be segmented and fitted simultaneously, and individual best-fitted shells can be clustered concurrently. The integration of the partition with the invariant analysis makes it possible to identify qualitative features from depth maps.
Source Title: Image and Vision Computing
ISSN: 02628856
DOI: 10.1016/0262-8856(95)01053-X
Appears in Collections:Staff Publications

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