• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Fibres & textiles in Eastern Europe >Developing an Intelligent Model for the Construction a Hip Shape Recognition System Based on 3D Body Measurement
【24h】

Developing an Intelligent Model for the Construction a Hip Shape Recognition System Based on 3D Body Measurement

机译：开发基于3D人体测量的髋关节形状识别系统智能模型

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The purpose of this paper was to develop an intelligent recognition system consisting of a feature reduction method combining cluster and correlation analyses, and a probabilistic neural network (PNN) classifier to identify different types of hip shape from 3D measurement for each person. Firstly 28 items reflecting lower body part information of 300 female university students aging from 20 to 24 years were selected. The feature reduction method was employed to extract typical indices. Secondly hip shapes were subdivided into five types by a K-means, cluster and analysis of variance (ANOVA). Finally the PNN was then trained to serve as a classifier for identifying five different hip shape types. The average classification accuracy of the scheme proposed was 97.37%, and its effectiveness was successfully validated by comparing with the BP and Support Vector Machine (SVM) scheme. Thus an intelligent recognition system was developed to make hip shape type classification of high precision and time saving.
机译：本文的目的是开发一种智能识别系统，该系统由结合了聚类分析和相关分析的特征约简方法以及一个概率神经网络（PNN）分类器组成，以从每个人的3D测量中识别出不同类型的臀部形状。首先，选择了反映300名20至24岁女大学生下半身信息的28个项目。采用特征约简方法提取典型指标。其次，通过K均值，聚类和方差分析（ANOVA）将髋部形状细分为五种类型。最终，然后训练了PNN作为分类器，以识别五种不同的髋关节形状类型。所提方案的平均分类准确率为97.37％，通过与BP和支持向量机（SVM）方案的比较，成功验证了其有效性。因此，开发了一种智能识别系统，以使髋骨形状类型分类具有较高的精度和节省的时间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号