SOIM: a self-organizing invertible map with applications in active vision

We propose a novel neural network, called the self-organized invertible map (SOIM), that is capable of learning many-to-one functionals mappings in a self-organized and online fashion. The design and performance of the SOIM are highlighted by learning a many-to-one functional mapping that exists in active vision for spatial representation of three-dimensional point targets. The learned spatial representation is invariant to changing camera configurations. The SOIM also possesses an invertible property that can be exploited for active vision. An efficient and experimentally feasible method was devised for learning this representation on a real active vision system. The proof of convergence during learning as well as conditions for invariance of the learned spatial representation are derived and then experimentally verified using the active vision system. We also demonstrate various active vision applications that benefit from the properties of the mapping learned by SOIM.