The authors consider the problem of detecting global symmetry in images consisting of dense arrangements of local features, such as dots or oriented segments. Images of this kind are encountered in textured scenes or field-type pictorial representations. The proposed computational model, elaborated on the basis of several psychophysical experiments, assumes a precedence of grouping over symmetry detection and consists of three stages: a grouping stage, in which clusters are formed among local elements presenting a sufficient level of mutual affinity; a symmetry detection stage, in which pairs of symmetrical clusters are discovered and their axes of symmetry determined; and a symmetry subsumption stage, in which an attempt is made to detect more global symmetries. An implementation of the model is described. Results are presented showing a good agreement of the model's performance with human symmetry perception.
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