Modeling visual search on a rough surface

Alasdair D. F. Clarke*, Mike J. Chantler, Patrick R. Green

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The LNL (linear, non-linear, linear) model has previously been successfully applied to the problem of texture segmentation. In this study we investigate the extent to which a simple LNL model can simulate human performance in a search task involving a target on a textured surface. Two different classes of surface are considered: 1/f(beta)-noise and near-regular textures. We find that in both cases the search performance of the model does not differ significantly from that of people, over a wide range of task difficulties.

Original languageEnglish
Article number11
Number of pages12
JournalJournal of Vision
Volume9
Issue number4
DOIs
Publication statusPublished - 13 Apr 2009

Keywords

  • linear-nonlinear-linear model
  • visual search
  • texture
  • texture analysis
  • Saccadic selectivity
  • computational model
  • eye-movements
  • direction
  • vision
  • target

Cite this

Clarke, A. D. F., Chantler, M. J., & Green, P. R. (2009). Modeling visual search on a rough surface. Journal of Vision, 9(4), [11]. https://doi.org/10.1167/9.4.11

Modeling visual search on a rough surface. / Clarke, Alasdair D. F.; Chantler, Mike J.; Green, Patrick R.

In: Journal of Vision, Vol. 9, No. 4, 11, 13.04.2009.

Research output: Contribution to journalArticle

Clarke, ADF, Chantler, MJ & Green, PR 2009, 'Modeling visual search on a rough surface', Journal of Vision, vol. 9, no. 4, 11. https://doi.org/10.1167/9.4.11
Clarke ADF, Chantler MJ, Green PR. Modeling visual search on a rough surface. Journal of Vision. 2009 Apr 13;9(4). 11. https://doi.org/10.1167/9.4.11
Clarke, Alasdair D. F. ; Chantler, Mike J. ; Green, Patrick R. / Modeling visual search on a rough surface. In: Journal of Vision. 2009 ; Vol. 9, No. 4.
@article{0dc10668278f44bea3ae4b21ff0412ac,
title = "Modeling visual search on a rough surface",
abstract = "The LNL (linear, non-linear, linear) model has previously been successfully applied to the problem of texture segmentation. In this study we investigate the extent to which a simple LNL model can simulate human performance in a search task involving a target on a textured surface. Two different classes of surface are considered: 1/f(beta)-noise and near-regular textures. We find that in both cases the search performance of the model does not differ significantly from that of people, over a wide range of task difficulties.",
keywords = "linear-nonlinear-linear model, visual search, texture, texture analysis, Saccadic selectivity, computational model, eye-movements, direction, vision, target",
author = "Clarke, {Alasdair D. F.} and Chantler, {Mike J.} and Green, {Patrick R.}",
year = "2009",
month = "4",
day = "13",
doi = "10.1167/9.4.11",
language = "English",
volume = "9",
journal = "Journal of Vision",
issn = "1534-7362",
publisher = "Association for Research in Vision and Ophthalmology Inc.",
number = "4",

}

TY - JOUR

T1 - Modeling visual search on a rough surface

AU - Clarke, Alasdair D. F.

AU - Chantler, Mike J.

AU - Green, Patrick R.

PY - 2009/4/13

Y1 - 2009/4/13

N2 - The LNL (linear, non-linear, linear) model has previously been successfully applied to the problem of texture segmentation. In this study we investigate the extent to which a simple LNL model can simulate human performance in a search task involving a target on a textured surface. Two different classes of surface are considered: 1/f(beta)-noise and near-regular textures. We find that in both cases the search performance of the model does not differ significantly from that of people, over a wide range of task difficulties.

AB - The LNL (linear, non-linear, linear) model has previously been successfully applied to the problem of texture segmentation. In this study we investigate the extent to which a simple LNL model can simulate human performance in a search task involving a target on a textured surface. Two different classes of surface are considered: 1/f(beta)-noise and near-regular textures. We find that in both cases the search performance of the model does not differ significantly from that of people, over a wide range of task difficulties.

KW - linear-nonlinear-linear model

KW - visual search

KW - texture

KW - texture analysis

KW - Saccadic selectivity

KW - computational model

KW - eye-movements

KW - direction

KW - vision

KW - target

U2 - 10.1167/9.4.11

DO - 10.1167/9.4.11

M3 - Article

VL - 9

JO - Journal of Vision

JF - Journal of Vision

SN - 1534-7362

IS - 4

M1 - 11

ER -