Articulated hand tracking by PCA-ICA approach
Articulated hand tracking by PCA-ICA approach
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DOI：
10.1109/fgr.2006.21
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发表时间：
2006-04
期刊：
影响因子：
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通讯作者：
Makoto Kato;Yenwei Chen;Gang Xu
中科院分区：
文献类型：
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作者：
Makoto Kato;Yenwei Chen;Gang Xu
本文介绍了一种新的手部运动表示跟踪和识别图像序列中的手指手势。人手有15个关节，其高维数使得手部运动建模变得困难。为了使事情更容易，在低维空间中表示手部运动是很重要的。主成分分析（PCA）被提出来降低维度。然而，PCA基向量仅表示全局特征，这对于表示内在特征不是最佳的。提出了一种基于独立分量分析（伊卡）的手部运动表示方法.伊卡基向量表示局部特征，每个特征对应于特定手指的运动。这种表示在建模手部运动以跟踪和识别图像序列中的手指手势方面更有效。本文通过对真实的图像序列中手部的跟踪，验证了该方法的有效性
This paper introduces a new representation of hand motions for tracking and recognizing hand-finger gestures in an image sequence. A human hand has 15 joints and its high dimensionality makes it difficult to model hand motions. To make things easier, it is important to represent a hand motion in a low dimensional space. Principle component analysis (PCA) has been proposed to reduce the dimensionality. However, the PCA basis vectors only represent global features, which are not optimal to represent intrinsic features. This paper proposes an efficient representation of hand motions by independent component analysis (ICA). The ICA basis vectors represent local features, each of which corresponds to the motion of a particular finger. This representation is more efficient in modeling hand motions for tracking and recognizing hand-finger gestures in an image sequence. This paper demonstrates the effectiveness of our method by tracking hands in real image sequences