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 and Reviews › RGC 21 - Publication in refereed journal › peer-review

10 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. © 2015 Elsevier Ltd

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

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Dots
Gestalt
Good continuation
Local symmetry
Non-accidentalness

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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. {\textcopyright} 2015 Elsevier Ltd",

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T2 - 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

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2016/9/1

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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. © 2015 Elsevier Ltd

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. © 2015 Elsevier Ltd

KW - Dots

KW - Gestalt

KW - Good continuation

KW - Local symmetry

KW - Non-accidentalness

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84948809075&origin=recordpage

U2 - 10.1016/j.visres.2015.09.004

DO - 10.1016/j.visres.2015.09.004

M3 - RGC 21 - Publication in refereed journal

C2 - 26408332

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

Bibliographical note

Research Keywords

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