Optimal Range Segmentation Parameters through Genetic Algorithms

A wide number of algorithms for surjtiace segmentation in range images have been recently proposed characterized by different approaches (edge filling, region growing, . . . ), different su$ace types (either for planar or curved suifaces) and different parameters involved. Optimization of the parameter set is a particularly critical task since the range of parameter variability is often quite large: parameter selection depends on surface type, sensors and the required speed which strongly affect peqormance. A framework for parameters optimization is proposed based on genetic algorithms. Such algorithms allow a general approach that has been succesfully applied on different state-of-the-art segmenters and different range image databases.