August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Measuring the visual salience of smooth paths by their non-accidentalness
Author Affiliations
  • Samy Blusseau
    CMLA, École Normale Supérieure de Cachan, Université Paris Saclay, France
  • Alejandra Carboni
    CIBPsi, Facultad de Psicología, Universidad de la República, Uruguay
  • Alejandro Maiche
    CIBPsi, Facultad de Psicología, Universidad de la República, Uruguay
  • Jean-Michel Morel
    CMLA, École Normale Supérieure de Cachan, Université Paris Saclay, France
  • Rafael Grompone von Gioi
    CMLA, École Normale Supérieure de Cachan, Université Paris Saclay, France
Journal of Vision September 2016, Vol.16, 303. doi:10.1167/16.12.303
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      Samy Blusseau, Alejandra Carboni, Alejandro Maiche, Jean-Michel Morel, Rafael Grompone von Gioi; Measuring the visual salience of smooth paths by their non-accidentalness. Journal of Vision 2016;16(12):303. doi: 10.1167/16.12.303.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

A well known result in psychophysical studies of good continuation in arrays of Gabor patches is that the visual system is better at detecting smooth paths rather than jagged ones, and all the more so as they are formed by elements that are roughly aligned to the local tangent of the contour (association field, Field et al. 1993). Here we present a similar experiment on contour detection, and a stochastic model that predicts and interprets the perceptual thresholds for this task, relying on the non-accidentalness principle. Our experiment consists in an attentive contour detection task in arrays of Gabor patches. The visibility of contours among the cluttered background is affected by three varying parameters: the number of target elements, the amount of angular noise deviating their orientations from the local tangents to the contour, and the total number of patches in the image. Sixteen subjects took the experiment and their detection performance was compared to an artificial observer algorithm, on every stimulus. We built this algorithm on the a-contrario theory (Desoneux et al. 2008), applied here to formalize mathematically the non-accidentalness principle for good continuation and proximity. To predict the salience of curves, it associates with each candidate percept a measure, the Number of False Alarms (NFA), quantifying its degree of masking. The NFA showed a strong correlation with detection performance: different targets with the same NFA yielded similar levels of dectability among subjects. Furthermore, the algorithm's answers matched accurately those of human subjects, on average as well as on a trial-by-trial basis. The overall results give credit to the non-accidentalness principle, as a way to interpret and predict the perceptual grouping in masking conditions. Future work will concentrate on predicting the salience of symmetry using the same framework.

Meeting abstract presented at VSS 2016

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