In this paper, a model is proposed for bilateral symmetry detection in images consisting of dense arrangements of local features. The model is elaborated on the basis of a psychophysical experiment showing that grouping precedes and facilitates symmetry detection. The proposed computational model consists of three stages: a grouping stage, a symmetry-detection stage, and a symmetry-subsumption stage. Reliance upon a preliminary grouping stage enables a significant reduction of the computational load for detecting symmetry. An implementation of the model is described, and results are presented, showing a good agreement of the model performance with human symmetry perception.