基于模板匹配的三维人体语义特征提取算法

随着社会经济发展，生活水平的提高，人们对定制产品的需求越来越多，例如服装高级定制、健身计划定制等。为更好地满足这些需求并提供精准的数据支撑，需提取精确的人体语义特征，即人体的身高、胸围、腰围等一系列参数。对现有特征提取算法进行分析研究，设计一种基于模板匹配的三维人体语义特征提取算法。通过模板模型逼近输入模型，将模板模型上的语义特征采样点拓展到输入模型上，使用NURBS曲线拟合采样点，计算曲线长度。实验结果表明本文所提算法综合性能好，能够为服装定制、人体动画、人体工程学设计等提供精确广泛的数据支持。

Semantic Feature Extraction Algorithm for 3D Human Body Based on Template Matching

With the development of society and economy and the improvement of living standards, people’s demand for customized products is increasing, such as advanced customization of clothing and customization of fitness programs. In order to better meet these needs and provide accurate data support, it is necessary to extract precise human body semantic features, that is, a series of parameters such as height, chest size, and waist size of the human body. The existing feature extraction algorithms are analyzed and researched, and a semantic feature extraction algorithm for 3D human body based on template matching is designed. The template model is used to approximate the input model, and the semantic feature sampling points on the template model are extended to the input model. The NURBS curve is used to fit the sampling points to calculate the curve length. The experimental results show that the proposed algorithm has good comprehensive performance and can provide accurate and extensive data support for clothing customization, human body animation, and ergonomic design.