Good continuation in dot patterns : A quantitative approach based on local symmetry and non-accidentalness
Resumen:
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 | |
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Gestalt Good continuation Dots Non-accidentalness Local symmetry Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://hdl.handle.net/20.500.12008/42664
https://doi.org/10.1016/j.visres.2015.09.004. |
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| Acceso abierto | |
| Licencia Creative Commons Atribución (CC - By 4.0) |
| Sumario: | 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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