论文标题
使用图形和非经典接受场进行轮廓集成
Contour Integration using Graph-Cut and Non-Classical Receptive Field
论文作者
论文摘要
许多边缘和轮廓检测算法给出了软值作为输出，最终的二进制图通常是通过应用最佳阈值获得的。在本文中，我们提出了一种新的方法来检测其他算法提取的边缘段的图像轮廓。我们的方法基于一个无向图形模型，将边段设置为顶点。所提出的能量功能受到主要视觉皮层中周围调制的启发，有助于抑制纹理噪声。我们的算法可以改善提取二进制图的提取，因为它考虑了其他重要因素，例如连通性，平滑度和长度的软值旁边。我们的定量和定性实验结果表明了该方法的功效。
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.
