17. The method of claim 1 , wherein said input signal is an input image, said hierarchical collection comprises a plurality of polynomial models, a centered step-edge model and a non-centered step-edge model, and wherein said extracting a clique of descriptors, said selecting a model to be recognized and said generating a new descriptor are performed during a plurality of iterations, wherein: in a first iteration, said clique of descriptors comprises a polynomial edge descriptor and said model to be recognized is said centered step-edge model; in a second iteration, said clique of descriptors comprises a second polynomial edge descriptor and a constant-gradient descriptor whose domain is on one side of the domain of said second polynomial edge descriptor, and said model to be recognized is said non-centered step-edge model; in a third iteration, said clique of descriptors comprises a third polynomial edge descriptor, a second and third constant-gradient descriptors whose domains are on opposite sides of said third polynomial edge descriptor, and said model to be recognized is said centered step-edge model.
1. A method to recognize model instances in an input signal, the method comprising the steps of: storing in a long-term memory a hierarchical collection of models, wherein: the instances of each model are represented by an ensemble of patterns; a non primitive pattern, defined on a domain and representative of an instance of a non-primitive model from said hierarchical collection has a fragmentation comprising a clique of local patterns; and wherein each local pattern, defined on a sub-domain of said domain, is representative of a local model from said hierarchical collection and provides an approximate representation of said non-primitive pattern within said sub-domain; generating, by a processing means, a plurality of descriptors wherein each of said descriptors specifies an estimated pattern representative of an instance of a model detected in said input signal; storing said descriptors in a short-term memory; extracting from said short-term memory, by said processing means, a clique of descriptors which specifies a clique of estimated patterns; selecting a model to be recognized from said hierarchical collection; and generating, by said processing means, and by using said clique of descriptors as auxiliary descriptors, a new descriptor that specifies a new pattern, wherein said new pattern has a fragmentation given by said clique of estimated patterns and is representative of an instance of said model to be recognized.