Digital diffusion network for image segmentation

Diffusion networks (i.e., networks with stochastic differential neuron equations) are attractive as means of implementing segmentation algorithms because of their potential for fast, parallel processing. However, the complexity of the required neuron circuitry makes many analog implementations impractical. In this paper, we consider a digital version of a diffusion network proposed by Wong (1991). The analog neuron equations are first discretized into versions driven by binary noise sequences. The discretized equations are then implemented with digital differential analyzers (DDAs) to produce a digital diffusion network. While much work remains to be done to finalize the digital network design, preliminary simulation test results of the digital network applied to image segmentation are encouraging