An improved neural network for segmenting objects' boundaries in real images
Wee Kheng Leow, Seet Chong Lua · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
An important task in object recognition is to first identify the boundaries of the objects in the input image. Several neural networks have been proposed to perform edge detection and boundary segmentation. Among them, Grossberg and Mingolla's (1985) boundary contour system (BCS) seems promising because it is able to complete missing object boundaries. Although BCS has been shown to work well on synthetic and silhouette images, we found that it has some shortcomings when applied to real images. This paper presents an improved version of BCS for handling the shortcomings.