Estimating 3D hand pose from a cluttered image
Estimating 3D hand pose from a cluttered image
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DOI：
10.1109/cvpr.2003.1211500
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发表时间：
2003-06
期刊：
影响因子：
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通讯作者：
V. Athitsos;S. Sclaroff
中科院分区：
文献类型：
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作者：
V. Athitsos;S. Sclaroff
提出了一种方法，可以生成最匹配输入图像的可信三维手的排列列表。手部姿态估计是一个图像数据库索引问题，其中输入的手部图像的最接近匹配从大型合成手部图像数据库中检索。与以前的方法相比，该系统可以在混乱的情况下工作，这要归功于两种新的容忍混乱的索引方法。首先，通过将二值边缘图像嵌入到高维欧几里德空间中，获得图像到模型倒角距离的计算效率近似。其次，通用的概率线匹配方法识别模型和输入图像之间的线段对应关系，这些线段对应关系最不可能偶然发生。在数百张真实手图像的定量实验中证明了该方法的性能。
A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclidean space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.