Modeling visual search on a rough surface

A. D F Clarke; Mike Chantler; Patrick R. Green

doi:10.1167/9.4.11

Modeling visual search on a rough surface

A. D F Clarke, Mike Chantler, Patrick R. Green

Research output: Contribution to journal › Article › peer-review

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^ß-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. © ARVO.

Original language	English
Article number	11
Pages (from-to)	1-12
Number of pages	13
Journal	Journal of Vision
Volume	9
Issue number	4
DOIs	https://doi.org/10.1167/9.4.11
Publication status	Published - 13 Apr 2009

Keywords

Linear-nonlinear-linear model
Texture
Visual search

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10.1167/9.4.11

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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{\ss}-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. {\textcopyright} ARVO.",

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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/fß-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. © ARVO.

KW - Linear-nonlinear-linear model

KW - Texture

KW - Visual search

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U2 - 10.1167/9.4.11

DO - 10.1167/9.4.11

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Modeling visual search on a rough surface

Abstract

Keywords

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