title: A model for global symmetry detection in dense images.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, Pavillon André-Aisenstadt, Ecole Polytechnique de Montréal, Québec, Canada.
authors.forename: F
authors.initials: F
authors.lastname: Labonté
authors.forename: Y
authors.initials: Y
authors.lastname: Shapira
authors.forename: P
authors.initials: P
authors.lastname: Cohen
authors.forename: J
authors.initials: J
authors.lastname: Faubert
mesh_terms: D003198:Computer Simulation; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D008433:Mathematics; D008954:Models, Biological; D010364:Pattern Recognition, Visual; D011601:Psychophysics; D013028:Space Perception / Q000502:physiology*