On the emulation of large-neighborhood templates with binary CNN-based architectures

This paper addresses the extension of applications covered by binary CNN-based architectures. The work is focused on diffusion-like tasks on binary images, traditionally tackled by either large neighborhood or propagating templates on CNNUM architecture. The solution adopted here is to split large neighborhood into smaller templates (3/spl times/3) on a binary CNN-based architecture. Trade-offs and hardware issues arisen from such an approach, as well as examples of application, are discussed throughout the paper.