In this paper a biologically motivated image flow processing mechanism is presented for visual exploration systems. The intention of this multi-channel topographic approach was to produce decision maps for salient feature localization and identification. As a unique biological study has recently confirmed mammalian visual systems process the world through a set of separate parallel channels and these representations are embodied in a stack of 'strata' in the retina. Beyond reflecting the biological motivations our main goal was to create an efficient algorithmic framework for real-life visual search and navigation experiments. In the course of this design the retinotopic processing scheme is embedded in an analogic Cellular Neural Network (CNN) algorithm where image flow is analyzed by temporal, spatial and spatio-temporal filters. The output of these sub-channels is then combined in a programmable configuration to form the new channel responses. In the core of the algorithm crisp or fuzzy logic strategies define the global channel interaction and result in a unique binary image flow. This processing mechanism of the algorithmic framework and the hardware architecture of the system are presented along with experimental ACE4k CNN chip results for several video flows recorded in flying vehicles.