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中文摘要：
使用基于K-means思想的KS主曲线算法，对国家人休尺寸测量工作中产生的带噪声扫描线数据集合进行非线性拟合，并依据曲率分布特征提出对目标特征点空间位置进行模糊分区估计的方法；同时提出了变量化模糊分区的优化策略，大幅度地提高了局部特征区域搜索的效率．
英文摘要：
Segmentation and extraction of the target feature points from the unregistered, noisy data set of human body scanning and measuring are discussed. We have filled the noise scanned data line with KS principal curve, which is based on the K-means theory. The crossing zero of curvature was used to segment and estimate the position of target feature points. And optimization scheme named variable fuzzy partition is also presented to improve the searching for local feature area.
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