A methodology for the characterization of the performance of thinning algorithms

The authors measure the performance of thinning algorithms in the ideal world of noise-free Blum ribbons. Differences in the performance of thinning algorithms emerge even when they are applied on noise-free ribbon images. The authors define an error criterion function based on the Hausdorf distance that measures the deviation between the ideal and actual output of the thinning algorithms. They illustrate the process of performance evaluation by the application of ten state of the art thinning algorithms to the same (large) set of input images. They present results that show the mean value of the normalized error for each algorithm over each population of images. How performance of an algorithm varies over different populations of images is examined