Boundary detection by artificial neural network

Abstract

'Active contour model - Snake', firstly suggested by Kass et al. in 1988, is a boundary detection scheme, which is known to be very effective for detecting boundary problematic to existing classical schemes. However, the requirement of heavy computation limited its practical application. Neural network, having a massively parallel architecture and being capable of processing huge amount of information in parallel manner, provides an alternative platform for real-time processing. In this paper, the 'Snake' formulation is first mapped to a generalized higher-order Hopfield network and finally a tunneling network, an alternative neural network suggested by Cheung & Lee in 1992, is adopted for the 'Snake' boundary detection scheme. Simulation performed manifests its feasibility and it's found that the solution obtained is better than some existing 'Snake' implementation.