Sensing the third dimension in stereo vision systems: a cellular neural networks approach
In this paper the use of the cellular neural network (CNN) paradigm is investigated for the vision-based real-time guidance of robots. This paradigm is employed in recovering information on the tridimensional structure of the environment, through the resolution of the static and the lateral motion stereo vision problems. The proposed approaches exploit the spontaneous internal energy decrease of the CNN, coding the problem in terms of an optimisation task. Results of computer simulations on some test cases for the two different issues are provided. The performance of a hardware implementation of these networks for the tasks presented is outlined.
cellular neural networks optimisation autonomous navigation computer vision stereo matching three-dimensional reconstruction