A computer with intelligent visual skills comparable to biological ones has been an ambitious research goal ever since the field of computer vision took off some 30 years ago. As an alternative to mathematical approaches, especially when they fall short of their expectations, attempts to mimic the processing of the brain have been pursued, but implementing the result into computer vision systems is still in its infancy. This project envisages the development of biologically-inspired computer vision techniqu es, which exploits the physiology and anatomical connectivity of individual visual neurons and integrates their individual functionalities into cooperative visual systems. Our approach will emphasis the role of the topological organization of visual neuron s with similar functional properties. The novelty of this approach lies in the direct involvement of the neuron's topological organization ("spatial layout") for the processing of visual information. Two types of visual neuron functionalities ("channels") will be modelled and integrated: the orientation selectivity leading to selective edge detection and the perception of 3-dimensional motion. These two functionalities will be linked together forming a cooperative multi-channel system for selective object d etection under various environmental conditions. The capability of newly developed computer vision techniques will be tested with images of rapidly changing scenes such as those observed by a car driver. This project is in the rapidly evolving area of biol ogically-motivated computer vision, and at the cross road of computational neuroscience and computer vision. It has a high scientific risk since multi-channel modelling has been tried before, but failed to proliferate computer vision. The project has a sig nificant technical potential to contribute to the development of a new breed of biologically-motivated driver assistance systems that issue visual warning and haptic signals to the driver.