Implementation of Visual Attention System Using Artificial Retina Chip and Bottom-Up Saliency Map Model

Abstract

This paper proposes a new hardware system for visual selective attention, in which a neuromorphic silicon retina chip is used as an input camera and a bottom-up saliency map model is implemented by a Field-Programmable Gate Array (FPGA) device. The proposed system mimics the roles of retina cells, V1 cells, and parts of lateral inferior parietal lobe (LIP), such as edge extraction, orientation, and selective attention response, respectively. The center surround difference and normalization for mimicking the roles of on-center and off-surround function in the lateral geniculate nucleus (LGN) are implemented by the FPGA. The integrated artificial retina chip with the FPGA successfully produces the human-like visual attention function, with small computational overhead. In order to apply this system to mobile robotic vision, the proposed system aims to low power dissipation and compactness. The experimental results show that the proposed system successfully generates the saliency information from natural scene.