基于新的重要性测度的数字平面曲线多边形逼近
Polygonal approximation of digital planar curve using novel significant measure
摘要：
本文提出了一种数字图像边界多边形逼近的迭代光顺技术。该技术从曲线的最精细的初始分割点开始。通过计算每个初始分割点的显著性测度来确定初始分割点对保持图像边界原始形状的贡献。所提出的方法区分的情况下，当曲线上的两点之间的曲线上的一个点直接投影在线段或超出此线段。它不仅识别这些情况，而且还以不同的方式计算其对这些情况的重要贡献。这种情况下的具体治疗允许保留点与高曲率，即使修订的一组主导点的推导。实验结果表明，所提出的技术与最先进的技术竞争。
英文摘要：
This paper presents an iterative smoothing technique for polygonal approximation of digital image boundary. The technique starts with finest initial segmentation points of a curve. The contribution of initially segmented points towards preserving the original shape of the image boundary is determined by computing the significant measure of every initial segmentation points which is sensitive to sharp turns, which may be missed easily when conventional significant measures are used for detecting dominant points. The proposed method differentiates between the situations when a point on the curve between two points on a curve projects directly upon the line segment or beyond this line segment. It not only identifies these situations, but also computes its significant contribution for these situations differently. This situation-specific treatment allows preservation of points with high curvature even as revised set of dominant points are derived. The experimental results show that the proposed technique competes well with the state of the art techniques.
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