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dc.contributor.advisorChantler, Mike J.
dc.contributor.advisorGreen, Patrick R.
dc.contributor.authorClarke, Alasdair Daniel Francis
dc.date.accessioned2011-03-10T11:22:30Z
dc.date.available2011-03-10T11:22:30Z
dc.date.issued2010-06
dc.identifier.urihttp://hdl.handle.net/10399/2351
dc.description.abstractMuch work has been done on developing algorithms for automated surface defect detection. However, comparisons between these models and human perception are rarely carried out. This thesis aims to investigate how well human observers can nd defects in textured surfaces, over a wide range of task di culties. Stimuli for experiments will be generated using texture synthesis methods and human search strategies will be captured by use of an eye tracker. Two di erent modelling approaches will be explored. A computational LNL-based model will be developed and compared to human performance in terms of the number of xations required to find the target. Secondly, a stochastic simulation, based on empirical distributions of saccades, will be compared to human search strategies.en_US
dc.language.isoenen_US
dc.publisherHeriot-Watt Universityen_US
dc.publisherMathematical and Computer Scienceen_US
dc.rightsAll items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.
dc.titleModelling visual search for surface defectsen_US
dc.typeThesisen_US


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