Autonomous clustering of fractal patterns via Hausdorff potentials

A nondeterministic kinetics is introduced in image plane for autonomous clustering of fractal attractors associated with contraction mappings. By reformulating 2D Newton potential in terms of the Hausdorff distance, both the attribution to fixed points of contraction mappings and the consistency of fixed point estimates are evaluated. Attracted by fixed point estimates, feature points are aggregated to successively organize discrete clusters structurally consistent with the mapping set. The proposed scheme was implemented and verified through simulation studies.