Contour segmentation with recurrent neural networks of pulse-coding neurons

Abstract

The performance of technical and biological vision systems crucially relies on powerful processing capabilities. Robust object recognition must be based on representations of segmented object candidates which are kept stable and sparse despite the highly variable nature of the environment. Here, we propose a network of pulse-coding neurons based on biological principles which establishs such representations using contour information. The system solves the task of grouping and figureground segregation by creating flexible temporal correlations among contotrr extracting units. In contrast to similar previous approaches, we explicitly address the problem of processing grey value images. In our multi-layer architecture, the extracted contour features are edges, line endings and vertices which interact by introducing facilatory and inhibitory, couplings among feature extracting neurons. As the result of the network dynamics, individual mutually occluding objects become defined by temporally correlated activity on contour representations.