Active Vision driven by a Neuromorphic Selective Attention System

In real world scenarios, guiding vision to focus on salient parts of the visual space is a computationally demanding tasks. Selective attention is a biologically inspired strategy to cope with this problem, that can be used in engineered systems with limited resources. In active vision systems however, the stringent realtime requirements limit the space of solutions that can be achieved with conventional machine vision techniques and systems. We propose a hybrid approach where we combine a custom neuromorphic VLSI saliency-map based attention system with a conventional imager and a workstation, to implement both fast contrast-based saccadic eye movements in parallel with standard machine vision attention models using high-resolution color input images. We describe the system and present its response properties using basic control experiments.