Notice of RetractionA hybrid genetic algorithm in PBRDF modeling

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

The polarized light scattered by the surface of a material contains information that can be used to describe the properties of the surface. Polarized Bidirectional Reflectance Distribution Function (PBRDF) is one of the most important factors used to represent the property of the surface. Because there is complex nonlinear relationship between the experimental results and model parameters, genetic algorithm is used to retrieve the model parameters. One drawback of the traditional genetic algorithm is that the convergence speed is slow and easy to fall into the local minimization. On the base of the traditional genetic algorithm to retrieve the parameters, simulated annealing (SA) algorithm is used to optimize the modeling of the PBRDF. The model for PBRDF and the designation of the hybrid algorithm is given in detail. For one typical painted surface, both the experiment results and the model calculation results are given. The calculation results of the model are demonstrated consistent well with the experimental results. The error convergence curve shows that, the hybrid genetic algorithm can avoid falling into the local minimization, and shorten the running time for the target function. Therefore, it is applicable used as a reference for target feature extraction and recognition in the future.