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.

A new technique for the fixation task is proposed. The proposed technique is view-based and uses a gradient descent technique in order to maximize an intensity-based similarity measure between the reference image and the currently grabbed image during control time. A key constraint for the development of computational mechanisms is the achievement of real-time capability on low-cost hardware. We utilize the complex-logarithmic mapping of input images into log-polar space. In addition to a high compression rate this also supports better fixation performance using a space-variant image representation of the image. We further suggest a confidence based similarity measure to overcome the problem of distortions and partial occlusions during the movement of the camera which is the main problem of view-based approaches. We demonstrate the practicability of our method in a real world experiment which solves the fixation task at a frame rate of 16 Hz on an Intel P200 architecture without any special image processing hardware.