Online learning for bootstrapping of object recognition and localization in a biologically motivated architecture

Abstract

We present a modular architecture for recognition and localization of objects in a scene that is motivated from coupling the ventral ("what") and dorsal ("where") pathways of human visual processing. Our main target is to demonstrate how online learning can be used to bootstrap the representation from nonspecific cues like stereo depth towards object-specific representations for recognition and detection. We show the realization of the system learning objects in a complex real-world environment and investigate its performance. © 2008 Springer-Verlag Berlin Heidelberg.