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- W2469185625 abstract "Over the past years, many application technologies related to Human Computer Interaction (HCI) have been developed for intelligent devices and wearable sensors. Moreover, they also have been received attention from both the research commu-nity and the industry. Almost of them make use of hand infor-mation such as hand gesture and posture. Consequently, the technical performance is highly depends on the information of humans’ hand and also, the number of products equipped with these technologies rapidly increases. Therefore, the accurate estimation of hand pose becomes a crucial issue to improve the performance of HCI applications. To detect landmarks, many algorithms have been develop-ing in this research field. Most of them are based on the pixel-based classification (1) and deformable part models (DPM) (2). However, they does not fit well in finger pose estimation since there are large pose variations due to camera viewpoints and various finger gestures. It is challenging to precisely detect the principal landmarks of fingers, as they flexibly deform with a large degree of freedom.In this paper, the regression based approach is used for esti-mating 3D multi-finger landmarks. Cascaded regression ap-proaches (3, 4) already used for hand pose estimation. Our ap-proach, however, concentrates on hierarchical structure of the detection strategy and initialization for better accuracy and stability. In our method, the bi-directional search structure is used for estimate landmarks with random forest regression. To" @default.
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- W2469185625 date "2016-06-21" @default.
- W2469185625 modified "2023-09-24" @default.
- W2469185625 title "Multi-Finger 3D Landmark Detection using Bi-Directional Hierarchical Regression" @default.
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- W2469185625 doi "https://doi.org/10.18204/jissis.2016.3.1.009" @default.
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