Second order CNN arrays for estimation of time-to-contact

This paper describes a cellular neural network (CNN) for estimating the time-to-contact from a one dimensional image. The CNN arrays used for this algorithm consist of cells with second order dynamics. The key feature of these arrays is that the spatial information in a region around each cell is represented by the phase of a complex number. The velocity is encoded as the temporal variation of that phase. By modelling this variation using adaptive temporal oscillators, the velocity can be estimated. Velocity information extracted over the entire array can be combined to estimate time-to-contact.