Image segmentation based on boundary constraint neural network

Abstract

Artificial neural networks based image segmentation methods have gained more acceptance over other methods because of their distributed architectures allowing real-time implementation. Another important advantage of the neural networks is that their robustness structures to the unexpected behavior of input image such as noise. On the other hand, the disadvantage of the neural networks is that the learning phase could be too long, and the resulting segmentation has a noisy boundary. The chapter also investigates a neural network based image segmentation method called constraint satisfaction neural network. It proposes a modification of constraint satisfaction neural network (CSNN) to alleviate both problems. It has been observed that when the edge field is brought in as a constraint, the convergence improves and the boundary noise is reduced.