• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Virtual Reality and Visualization >Automatic Human Body Feature Extraction and Size Measurement by Random Forest Regression Analysis of Geodesics Distance
【24h】

Automatic Human Body Feature Extraction and Size Measurement by Random Forest Regression Analysis of Geodesics Distance

机译：大气距离随机森林回归分析自动人体特征提取和尺寸测量

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

It is a pervasive problem to obtain the human body size without contacting for apparel application. In this paper, a new approach is proposed which can be applied to calculate human body size such as shoulder width, bust, hips and waist girth with single depth camera. First, single depth camera as the 3D model acquisition device was used to get the 3D human body model. Then an automatic extraction method of focal features on 3D human body via random forest regression analysis of geodesics distance is used to extract the predefined feature points and lines. Finally, the individual human body size is calculated according to the feature points and lines. The method is an automatic data-driven way. The scale-invariant heat kernel signature is exploited to serve as feature proximity. So it is insensitive to postures and different shapes of 3D human body. These main advantages of our method lead to a robust and accurate feature extraction technique and size measurement for 3D human bodies in various postures and shapes. The experiment results show that the average relative error of feature points extraction is 0.0617 cm. The average relative errors of shoulder width and girth are 1.332 cm and 0.7635 cm, respectively. Overall, the algorithm has a better detection effect for 3D human body size, and it is stable with better robustness.
机译：在不接触服装应用的情况下获得人体大小是一种普遍的问题。在本文中，提出了一种新方法，可以应用于计算人体尺寸，如肩宽，胸围，臀部和腰围，用单一深度相机。首先，使用单独的深度相机作为3D模型采集设备来获得3D人体模型。然后，通过大森林回归分析测地距离的3D人体焦点特征自动提取方法，用于提取预定义的特征点和线。最后，根据特征点和线来计算各个人体大小。该方法是一种自动数据驱动的方式。尺度不变的热内核签名被利用以作为特征接近。因此，它对3D人体的姿势和不同形状不敏感。我们方法的这些主要优点导致了各种姿势和形状的3D人体的稳健和准确的特征提取技术和尺寸测量。实验结果表明，特征点提取的平均相对误差为0.0617厘米。肩宽和周长的平均相对误差分别为1.332厘米和0.7635厘米。总的来说，该算法对3D人体尺寸具有更好的检测效果，具有更高的稳健性稳定。

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