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.
authors.affiliation: The Texture Lab, School of Mathematics and Computer Science, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK. adfc1@hw.ac.uk
authors.forename: Alasdair D F
authors.initials: AD
authors.lastname: Clarke
authors.forename: Mike J
authors.initials: MJ
authors.lastname: Chantler
authors.forename: Patrick R
authors.initials: PR
authors.lastname: Green
mesh_terms: D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D014796:Visual Perception / Q000502:physiology*