Modelling visual search for surface defects
Abstract
Much 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.