A GA-based integrated approach to model-assisted matching and pose estimation for automated visual inspection applications

We present a genetic algorithm based integrated approach to model-assisted matching and pose estimation for automated visual inspection applications. Unlike the past works reported in literature, this approach does not consider the matching between the model and the image of the object to be essential step prior to pose estimation. A set of matched vertices sequence and poses are hypothesized using a newly proposed composite chromosome structure and these are genetically evolved until a reasonably accurate pose is determined. Our algorithm demonstrates its robustness against noise as well as missing and spurious object vertices.