Brain-inspired model for multiagent semantic image interpretation
Mihaela Luca, Violeta Tulceanu · 2016
We propose a semantic model of interpreting and expressing the contents of images based on a simplified mathematical model of human reasoning, derived from data captured by brain-computer interfacing. Our goal is to allow a mobile agent to capture images and transmit in abstract sentences of a formal language its basic observations to a human or to another intelligent agent. The model permits learning from and communication with other agents, as it is a high-level formalism, independent of agent representation of image. This enables sensor fusion and inter-agent interpretation of input. The visible universe and image content can be understood via the agent’s personal observations and/or sentences sent by other agents. The model is applicable for smart home surveillance and street surveillance.