Statistical morphological skeleton for representing and coding noisy shapes

A new shape descriptor obtained by skeletonisation of noisy binary images i s presented. Skeleton extraction is performed by using an algorithm based o n a new class of parametrised binary morphological operators, taking into a ccount statistical aspects. Parameters are adaptively selected during the s uccessive iterations of the skeletonisation operation to regulate the chara cteristics of the shape descriptor. A probabilistic interpretation of the s cheduling strategy used for parameters is proposed by analogy to stochastic optimisation techniques. Skeletonisation results on patterns extracted by a change-detection method in a visual-based surveillance application are re ported. Results show the greater robustness of the proposed method as compa red with other morphological approaches.