基于深度数据的实时手势特征提取

Haozhe Huang, Zhaojie Ju, Honghai Liu
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引用次数: 3

摘要

本文提出了一种从微软Kinect捕获的RGB-D图像中实时提取手势特征的新方法。提出了一种基于轮廓长度信息的去噪方法,用于手势平滑分割和边缘轮廓提取。在此基础上,提出了一种基于手指移动距离算法的手掌图像定位和指尖特征提取方法。特别是Lasso算法能够有效地从手部轮廓曲线中正确提取指尖特征,具有良好的实时性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time hand gesture feature extraction using depth data
In this paper, a novel method is proposed to extract hand gesture features in real-time from RGB-D images captured by the Microsoft's Kinect. A contour length information based de-noise method is introduced for the hand gesture smooth segmentation and edge contour extraction. In addition, a finger earth mover's distance algorithm is applied with a novel approach to locate the palm image and extract fingertip features. Especially the proposed Lasso algorithm can effectively extract the fingertip feature from a hand contour curve correctly with excellent real-time performance.
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