Contour Integration using Graph-Cut and Non-Classical Receptive Field
This work addresses the issue of noisy and disconnected contours in image processing, which is important for computer vision applications, though it appears incremental as it builds upon existing edge detection algorithms.
The paper tackled the problem of converting soft edge detection outputs into binary contour maps by proposing a graphical model that incorporates connectivity, smoothness, and length constraints, resulting in improved performance as shown in quantitative and qualitative experiments.
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.