3D object recognition from 2D invariant view sequence under translation, rotation and scale by means of ANN ensemble

In this paper, we present a supervised multiple-weight neural network ensemble strategy for 3D object recognition from 2D multiple-view invariant sequence, so as to achieve omnidirectional information accumulation or solution in large-scale database. View information with transition in explicitly temporal order, is empirically selected for training set. On condition that requirements could not be met to a certain extent in one 3D object, more complicated training set is adopted in order to regrow and expand knowledge until satisfactory, without affecting knowledge acquired previously in other 3D objects. Simulation experiment for 3D object recognition from 2D view sequence achieved encouraging results, and proved effective and feasible in the approach proposed.