Good continuation in dot patterns: A quantitative approach based on local symmetry and non-accidentalness

José LEZAMA; Gregory RANDALL; Jean-Michel MOREL; Rafael GROMPONE VON GIOI

doi:10.1016/j.visres.2015.09.004

Good continuation in dot patterns: A quantitative approach based on local symmetry and non-accidentalness

José LEZAMA^*, Gregory RANDALL, Jean-Michel MOREL, Rafael GROMPONE VON GIOI

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

9 Citations (Scopus)

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.

Original language	English
Pages (from-to)	183-191
Number of pages	9
Journal	Vision Research
Volume	126
Early online date	1 Oct 2015
DOIs	https://doi.org/10.1016/j.visres.2015.09.004
Publication status	Published - Sept 2016
Externally published	Yes

Funding

Work partly founded by the Centre National d’Etudes Spatiales (CNES, MISS Project), the European Research Council (advanced grant Twelve Labours), the Office of Naval Research (ONR Grant N00014-141-0023 ), DGA project Stéréo , and ANR-DGA project ANR-12-ASTR-0035 .

Keywords

Dots
Gestalt
Good continuation
Local symmetry
Non-accidentalness

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10.1016/j.visres.2015.09.004

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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.",

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language = "English",

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journal = "Vision Research",

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T1 - Good continuation in dot patterns: A quantitative approach based on local symmetry and non-accidentalness

AU - LEZAMA, José

AU - RANDALL, Gregory

AU - MOREL, Jean-Michel

AU - GROMPONE VON GIOI, Rafael

PY - 2016/9

Y1 - 2016/9

N2 - 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.

AB - 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.

KW - Dots

KW - Gestalt

KW - Good continuation

KW - Local symmetry

KW - Non-accidentalness

UR - https://www.scopus.com/pages/publications/84948809075

U2 - 10.1016/j.visres.2015.09.004

DO - 10.1016/j.visres.2015.09.004

M3 - Journal Article (refereed)

AN - SCOPUS:84948809075

SN - 0042-6989

VL - 126

SP - 183

EP - 191

JO - Vision Research

JF - Vision Research

ER -

Good continuation in dot patterns: A quantitative approach based on local symmetry and non-accidentalness

Abstract

Funding

Keywords

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