Extracting Perceptual Structure in Dot Patterns: An Integrated Approach

摘要

This paper presents a computational approach to perceptual grouping in dot patterns. Detection of perceptual organization is done in two steps. The first step called the lowest level grouping, extracts the perceptual segments of dots that group together because of their relative locations. The grouping is accomplished by interpreting dots as belonging to interior or border of a perceptual segment, or belonging to a perceived curve, or being isolated. To perform the lowest level grouping, first the geometric structure of the dot pattern is represented in terms of certain geometric properties of the Voronoi neighborhoods of the dots. The grouping is accomplished throgh independent modules that possess narrow expertise for recognition of typical interior dots, border dots, curve dots and isolated dots, from the properties of the Voronoi neighborhoods. The results of the modules are allowed to influence and change each other so as to result in perceptual components that satisfy global. Gestalt criteria such as border or curve smoothness and component compactness. Thus an integration is performed of multiple constraints active at different perceptual levels and having different scopes in the dot pattern, to infer the lowest level perceptual structure. The result of the lowest level grouping phase is the partitioning of a dot pattern into different perceptual segments of tokens. The second step further groups the lowest level tokens to identify any hierarchical structure present.