A COMPUTATIONAL STRUCTURE FOR PREATTENTIVE PERCEPTUAL ORGANIZATION - GRAPHICAL ENUMERATION AND VOTING METHODS

We present an efficient computational structure for preattentive perceptual organization. By perceptual organization we refer to the ability of a vision system to organize features detected in images based on viewpoint consistency and other Gestaltic perceptual phenomena. This usually has two components, a primarily bottom up preattentive part and a top down attentive part, with meaningful features emerging in a synergistic fashion from the original set of (very) primitive features. In this work we advance a computational structure for preattentive perceptual organization. We propose a hierarchical approach, using voting methods to build associations through consensus and relational graphs to represent the organization at each level. The voting method is very efficient in terms of time and space and performs impressively for a wide range of organizations. The graphical representation allows the ready extraction of higher order features, or perceptual tokens, because the relational information is rendered explicit.