1. An adaptive artificial vision method comprising the following steps: (a) defining successive couples of synchronized timesteps (t −1, t; t, t &plus;1 ;... ) such that the time difference &tgr; between two synchronized timesteps (t −1, t; t, t &plus;1 ;... ) of a couple of synchronized timesteps is equal to a predetermined time delay &tgr; 0, (b) comparing two successive images (I t−1, I t ; I t, I t&plus;1,... ) at each couple of synchronized timesteps (t −1, t; t, t &plus;1 ;... ) spaced by said predetermined time delay &tgr; 0 for obtaining a delta image &Dgr; t which is the result of the computation of the distance between each pixel of said two successive images (I t−1, I t ; I t, I t&plus;1,... ) in view of characterizing movements of objects between said two successive images (I t−1, I t ; I t, I t&plus;1,... ), (c) extracting features from said delta image &Dgr; t for obtaining a potential dynamic patch P t which is compared with dynamic patches previously recorded in a first repertory R d which is progressively constructed in real time from an initial void repertory, (d) selecting the closest dynamic patch D i in the first repertory R d or if no sufficiently close dynamic patch still exists, adding the potential dynamic patch P t to the first repertory R d and therefore obtaining and storing a dynamic patch D i from the comparison of two successive images (I t−1, I t ; I t, I t&plus;1,... ) at each couple of synchronized timesteps (t −1, t; t, t &plus;1 ;... ), and (e) temporally integrating stored dynamic patches Di of the first repertory R d in order to detect and store stable sets of active dynamic patches representing a characterization of a reoccuring movement or event which is observed.