1. An adaptive artificial vision method comprising the following steps: (a) defining successive couples of synchronized timesteps (t −1 , t; t, t +1 ;. .. ) such that the time difference τ between two synchronized timesteps (t −1 , t; t, t +1 ;. .. ) of a couple of synchronized timesteps is equal to a predetermined time delay τ 0 , (b) comparing two successive images (I t−1 , I t ; I t , I t+1 ;. .. ) at each couple of synchronized timesteps (t −1 , t; t, t +1 ;. .. ) spaced by said predetermined time delay τ 0 for obtaining a delta image Δ 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+1 ;. .. ) in view of characterizing movements of objects between said two successive images (I t−1 , I t ; I t , I t+1 ;. .. ), (c) extracting features from said delta image Δ 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 this first repertory R d or if not 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+1 ;. .. ) at each couple of synchronized timesteps (t −1 , t; t, t +1 ;. .. ), and (e) temporally integrating stored dynamic patches D i of the first repertory R d in order to detect and store stable sets of active dynamic patches representing a characterization of a reoccurring movement or event which is observed.