14. The method of claim 1 , wherein: said input signal is an input image, the domain of said patterns are two-dimensional image regions; said hierarchical collection comprises a plurality of polynomial models and a centered step-edge model; said non-primitive model is said centered step-edge model; said clique of local patterns includes a cubic edge pattern; said model to be recognized is said centered step-edge model; said clique of descriptors includes a cubic-edge descriptor calculated from said input image; and said new pattern is a step-edge pattern estimated by utilizing said cubic-edge descriptor as auxiliary descriptor.
14. The method of claim 1 , wherein: said input signal is an input image, the domain of said patterns are two-dimensional image regions; said hierarchical collection comprises a plurality of polynomial models and a centered step-edge model; said non-primitive model is said centered step-edge model; said clique of local patterns includes a cubic edge pattern; said model to be recognized is said centered step-edge model; said clique of descriptors includes a cubic-edge descriptor calculated from said input image; and said new pattern is a step-edge pattern estimated by utilizing said cubic-edge descriptor as auxiliary descriptor. 15. The method claim 14 , wherein: said hierarchical collection further comprises a junction model and a non-centered step-edge edge model; a first junction pattern has a fragmentation comprising a local centered step-edge pattern; a second junction pattern has a fragmentation comprising a local non-centered step-edge pattern.