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.