基于图像过渡网络的手部形状估计

Y. Hamada, N. Shimada, Y. Shirai
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引用次数: 13

摘要

提出了一种基于双相机剪影图像的手部姿态估计方法。首先,我们提取了一对图像的轮廓轮廓。我们从不同姿势的手图像中构造一个特征空间。为了有效匹配,我们为每个图像定义形状复杂度,以查看形状特征的表示程度。对于一对输入图像,根据形状复杂度将两种匹配误差组合计算总匹配误差。从而得到一对图像的最优匹配图像。为了快速处理,我们利用形状变化的约束来限制匹配候选者。可能的形状转换用转换网络表示。因为网络很难构建,所以我们采用离线学习,通过展示手部形状序列的例子来自动创建节点和链接。我们展示了构建过渡网络的实验和使用该网络的匹配性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hand shape estimation using image transition network
We present a method of hand posture estimation from silhouette images taken by two cameras. First, we extract the silhouette contour for a pair of images. We construct an eigenspace from images of hands with various postures. For effective matching, we define a shape complexity for each image to see how well the shape feature is represented. For a pair of input images, the total matching error is computed by combining the two matching errors according to the shape complexity. Thus the best-matched image is obtained for a pair of images. For rapid processing, we limit the matching candidate by using the constraint on the shape change. The possible shape transition is represented by a transition network. Because the network is hard to build, we apply offline learning, where nodes and links are automatically created by showing examples of hand shape sequences. We show experiments of building the transition networks and the performance of matching using the network.
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