An Improved Live-Wire Freed from the Restriction of the Direct Line Between Seed Points

Zhou Di, Xu Wenbo
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Abstract

In the original live-wire algorithm, optimality is defined as the minimum cumulative cost path from a seed point to another, where the cumulative cost of a path is the sum of the local edge costs on the path. Therefore, the optimal path between two seed points is restricted not far away from the straight line between these two points. Consequently, when delineating a boundary with acute concaves and convexes, more seed points are required which can become very time consuming to calculate. This paper proposed an improved live-wire algorithm where the optimality is redefined as the minimum average cost path between two seed points, which frees the optimal path from the restriction of the straight line. In theory, the original live-wire algorithm is only a special case of our improved algorithm. Furthermore, the Canny edges on the boundary of the target object is weighted according to the gray or color value of the seed point. Experiments conducted on a variety of image types have shown that this improved live-wire algorithm requires less seed points than the original algorithm when delineating the same boundary and as a result, reduces the time required to complete the calculation.
一种不受种子点间直线限制的改进活线
在原始的活线算法中,最优性被定义为从一个种子点到另一个种子点的累积代价最小的路径,其中路径的累积代价是路径上局部边代价的总和。因此,两个种子点之间的最优路径被限制在距离这两个种子点之间的直线不远的地方。因此,当描绘具有尖锐凹凸的边界时,需要更多的种子点,这可能变得非常耗时。本文提出了一种改进的活线算法,将最优性重新定义为两个种子点之间的最小平均代价路径,使最优路径从直线的限制中解放出来。理论上,原活线算法只是我们改进算法的一个特例。然后根据种子点的灰度值或颜色值对目标物体边界上的Canny边缘进行加权。在多种图像类型上进行的实验表明,改进的live-wire算法在划定相同边界时所需的种子点比原算法少,从而减少了完成计算所需的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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