Space-variant dynamic neural fields for visual attention

In this paper we propose a new method for the fast application of dynamic neural fields (DNF) by utilizing the data reduction properties of space-variant active vision (SVAV). We apply this method to the control of visual attention. Dynamic neural fields have several advantages which are useful for many robot vision tasks, e.g. navigation or gaze-control. The dynamics of lateral interaction between neural units generates well-localized areas of high neural activation, which can be easily detected and used for behavior selection. The major focus of this paper is to drastically reduce the computational expense for the application of two-dimensional DNF. For that purpose, the dynamics of DNF is transformed into a space-variant field representation, defining a new type of DNF, namely space-variant dynamic neural fields (SVDNF). The effectiveness of the proposed method is demonstrated for our integrated monocular space-variant vision system. This system uses SVAV for real-time fixation control, depth-from motion estimation and SVDNF for the control of visual attention