A Fast Joint Bioinspired Algorithm for Optic Flow and Two-Dimensional Disparity Estimation

The faithful detection of the motion and of the distance of the objects in the visual scene is a desirable feature of any artificial vision system designed to operate in unknown environments characterized by conditions variable in time in an often unpredictable way. Here, we propose a distributed neuromorphic architecture, that, by sharing the computational resources to solve the stereo and the motion problems, produces fast and reliable estimates of optic flow and 2D disparity. The specific joint design approach allows us to obtain high performance at all affordable computational cost. The approach is validated with respect to the state-of-the-art algorithms and in real-world situations.