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- W2049662279 abstract "In this paper, we present a multilevel grouping scheme that groups primitive image features based on properties of perceptual organization, and groups higher-level structures derived from prior grouping to more closely approximate complex objects for recognition. Typically, the choice of candidates for grouping is controlled by thresholds on a set of criteria. Our scheme replaces the thresholds by fuzzy sets, so that the degree of membership in a grouping criterion set reflects the degree to which a particular element or set of elements fits the criterion. By adjusting these memberships, the degree of approximation in grouping can be controlled, allowing for more robust higher-level reasoning. This paper addresses both lower-level and higher-level grouping. At the lower level, specific membership functions are defined for grouping according to collinearity, parallelism, symmetry, and junction. Higher-level grouping strategies are described for three recognition tasks; extracting curves, extracting natural branching structure, and extracting polyhedral objects." @default.
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- W2049662279 date "1994-08-01" @default.
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- W2049662279 title "Characterizing and controlling approximation in hierarchical perceptual grouping" @default.
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- W2049662279 doi "https://doi.org/10.1016/0165-0114(94)90020-5" @default.
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