Event-driven proto-object based saliency in 3D space to attract a robot's attention.

ABSTRACT: To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features and objects. Current depth-based proto-object attention models have been implemented for standard RGB-D cameras that produce synchronous frames. In contrast, event cameras are neuromorphic sensors that loosely mimic the function of the human retina by asynchronously encoding per-pixel brightness changes at very high temporal resolution, thereby providing advantages like high dynamic range, efficiency (thanks to their high degree of signal compression), and low latency. We propose a bio-inspired bottom-up attention model that exploits even

SUBMITTER: Ghosh S 

PROVIDER: S-EPMC9090933 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature