Zach Christopher, Shan Liang, Niethammer Marc
Pattern Recognit DAGM. 2009;5748:552-561. doi: 10.1007/978-3-642-03798-6_56.
We present a continuous and convex formulation for Finsler active contours using seed regions or utilizing a regional bias term. The utilization of general Finsler metrics instead of Riemannian metrics allows the segmentation boundary to favor appropriate locations (e.g. with strong image discontinuities) and suitable directions (e.g. aligned with dark to bright image gradients). Strong edges are not required everywhere along the desired segmentation boundary due to incorporation of a regional bias. The resulting optimization procedure is simple and efficient, and leads to binary segmentation results regardless of the underlying continuous formulation. We demonstrate the proposed method in several examples.
我们提出了一种用于芬斯勒活动轮廓的连续且凸的公式化方法,该方法使用种子区域或利用区域偏差项。使用一般的芬斯勒度量而非黎曼度量,使得分割边界倾向于合适的位置(例如具有强烈图像不连续性的位置)和合适的方向(例如与图像从暗到亮的梯度对齐)。由于纳入了区域偏差,沿期望分割边界并非处处都需要强边缘。由此产生的优化过程简单高效,并且无论底层的连续公式如何,都能得到二值分割结果。我们在几个示例中展示了所提出的方法。