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Fuzzy Clustering of Female Body Shape and Identification of Side Parts Characteristics

机译:女性身体形态的模糊聚类与侧身特征的识别

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摘要

The development and application of virtual human body reconstruction required distinguish human bodily form accurately. At present, most of the analysis of the human body was based on body cross-section and circumference characteristics, which can not well reflect the human body curves. This paper analyzed the characteristics of the human body side, using two indexes reflect female body side parts characteristics. According to statistical analysis, the two indexes relative membership functions were established. This paper classified 20 samples by the fuzzy classification and recognition model, and obtained the optimal fuzzy recognition matrix, the optimal fuzzy clustering central matrix and the reasonable weight vector. Then a model of the fuzzy clustering and recognition of female bodily form were built based on lateral part characteristic. Moreover, 25 female body data was analyzed with this model. Calculation results showed that using fuzzy partition clustering method to female side parts form characteristics of classification and recognition of the method was feasible.
机译:虚拟人体重构的开发和应用需要准确区分人体形态。目前,对人体的大多数分析都是基于人体的横截面和周长特征,无法很好地反映人体曲线。本文分析了人体侧面特征,利用两个指标反映了女性侧面部位的特征。通过统计分析,建立了两个指标的相对隶属度函数。通过模糊分类识别模型对20个样本进行分类,得到最优模糊识别矩阵,最优模糊聚类中心矩阵和合理权重向量。然后根据侧面特征建立了模糊聚类和女性身体形态识别模型。此外,使用该模型分析了25位女性的身体数据。计算结果表明,采用模糊分区聚类方法对女性侧肢部位形态特征进行分类和识别是可行的。

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