A comparison of CNN and LEGION networks

CNN and LEGION networks have been extensively studied in recent years. These two frameworks share many common features; both employ continuous-time dynamics, are nonlinear, and emphasize local connectivity. In addition, they both have been successfully applied to visual processing tasks and implemented on analog VLSI chips. This paper investigates the relations between the two frameworks. We present their standard versions, and contrast the underlying dynamics and connectivity. We also describe several tasks where both CNN and LEGION have been applied. The comparison reveals fundamental differences between them. CNN is good for early visual processing, whereas LEGION is good for midlevel visual processing. Furthermore, the comparison suggests that a combined network is likely to enhance the overall processing capability.