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- W2057658876 abstract "A nonparametric clustering algorithm, called cell mean shift (CMS), is developed to extract clusters of a set of points on the Gaussian sphere S2. It is computationally more efficient than the traditional mean shift (MS). Based on the singular value decomposition, the dimensional analysis is introduced to classify these clusters into point-, curve-, and area-form clusters. Each cluster is the Gaussian image of a set of points which will be examined by a connected search in R3. An orientation analysis of the Gaussian map to area-form clusters is applied to identify hyperbolic and elliptical regions. A signed point-to-plane distance function is used to identify points of convex and concave regions. Segmentation results of several real as well as synthetic point clouds, together with complexity analyses, are presented." @default.
- W2057658876 created "2016-06-24" @default.
- W2057658876 creator A5018073672 @default.
- W2057658876 creator A5066457130 @default.
- W2057658876 date "2008-05-01" @default.
- W2057658876 modified "2023-10-01" @default.
- W2057658876 title "Automatic segmentation of unorganized noisy point clouds based on the Gaussian map" @default.
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- W2057658876 doi "https://doi.org/10.1016/j.cad.2008.02.004" @default.
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