title: Good continuation in dot patterns: A quantitative approach based on local symmetry and non-accidentalness.
abstract: We propose a novel approach to the grouping of dot patterns by the good continuation law. Our model is based on local symmetries, and the non-accidentalness principle to determine perceptually relevant configurations. A quantitative measure of non-accidentalness is proposed, showing a good correlation with the visibility of a curve of dots. A robust, unsupervised and scale-invariant algorithm for the detection of good continuation of dots is derived. The results of the proposed method are illustrated on various datasets, including data from classic psychophysical studies. An online demonstration of the algorithm allows the reader to directly evaluate the method.
authors.affiliation: CMLA, ENS Cachan, France; IIE, Universidad de la República, Uruguay. Electronic address: lezama@cmla.ens-cachan.fr.
authors.forename: José
authors.initials: J
authors.lastname: Lezama
authors.affiliation: IIE, Universidad de la República, Uruguay.
authors.forename: Gregory
authors.initials: G
authors.lastname: Randall
authors.affiliation: CMLA, ENS Cachan, France.
authors.forename: Jean-Michel
authors.initials: JM
authors.lastname: Morel
authors.affiliation: CMLA, ENS Cachan, France.
authors.forename: Rafael
authors.initials: R
authors.lastname: Grompone von Gioi
mesh_terms: D000465:Algorithms; D005556:Form Perception / Q000502:physiology*; D005863:Gestalt Theory; D006801:Humans; D008960:Models, Psychological; D008962:Models, Theoretical; D010364:Pattern Recognition, Visual / Q000502:physiology*; D011601:Psychophysics
keywords: Dots; Gestalt; Good continuation; Local symmetry; Non-accidentalness