Multilevel grouping: combining bottom-up and top-down reasoning for object recognition
Hang-Bong Kang, Ellen Walker · 2002
Presents a multilevel grouping scheme that groups primitive image features based on properties of perceptual organization, and groups higher-level structures to more closely approximate complex objects for recognition. At each level, grouping is performed according to geometric relationships among the component objects. The authors' scheme represents the grouping criteria by fuzzy sets, so that the degree of membership in a grouping criterion set reflects the degree to which the objects fit the criterion. Examples of grouping functions are provided for both top-down and bottom-up grouping.