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• W3186153124 abstract "Abstract Robust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation." @default.
• W3186153124 created "2021-08-02" @default.
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• W3186153124 date "2021-07-19" @default.
• W3186153124 modified "2023-09-24" @default.
• W3186153124 title "Robust hand gesture recognition using multiple shape-oriented visual cues" @default.
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• W3186153124 doi "https://doi.org/10.1186/s13640-021-00567-1" @default.
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