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

doi:10.3969/j.issn.1006-2475.2020.04.016

Abstract

Abstract: 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.

Key words: 3D human model, feature extraction, template matching

CLC Number:

TP391.41

LI Ling-jie1, TONG Jing1,2, BU Wen-yu1, SUN Hai-zhou1, CHEN Zheng-ming1,2. Semantic Feature Extraction Algorithm for 3D Human Body Based on Template Matching[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.04.016.

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