Biologically motivated computationally intensive approaches to image pattern recognition

This paper presents some of the research activities of the research group in vision as a grand challenge problem whose solution is estimated to need the power of Tflop/s computers and for which computational methods have yet to be developed. The concerned approaches are biologically motivated, in that we try to mimic and use mechanisms employed by natural vision systems, more specifically the visual system of primates. Visual information representations which are motivated by the function of the primary visual cortex, more specifically by the function of so-called simple cells, are computed. Three different methods for using such representations to solve image pattern recognition problems are presented. These are: (i) extraction and comparison of lower-dimension representations, (ii) computing optimal mappings of an image onto other images by optic flow techniques and (iii) application of a seif-organising neural network classifier. The problems of automatic recognition and classification of visual patterns, in particular the discrimination of human faces, are used to test the usefulness and feasibility of these approaches.