  Many edge and contour detection algorithms give a soft-value as an output and
the final binary map is commonly obtained by applying an optimal threshold. In
this paper, we propose a novel method to detect image contours from the
extracted edge segments of other algorithms. Our method is based on an
undirected graphical model with the edge segments set as the vertices. The
proposed energy functions are inspired by the surround modulation in the
primary visual cortex that help suppressing texture noise. Our algorithm can
improve extracting the binary map, because it considers other important factors
such as connectivity, smoothness, and length of the contour beside the
soft-values. Our quantitative and qualitative experimental results show the
efficacy of the proposed method.
