scholarly journals Visual Information Sensing Technology. Multiple-Camera Based Hand Pose Estimation Method using Distance Transformation.

1997 ◽  
Vol 51 (12) ◽  
pp. 2116-2125 ◽  
Author(s):  
Akira Utsumi ◽  
Tsutomu Miyasato ◽  
Fumio Kishino ◽  
Jun Ohya ◽  
Ryohei Nakatsu
Keyword(s):  
Pose Estimation ◽  
Visual Information ◽  
Estimation Method ◽  
Distance Transformation ◽  
Multiple Camera ◽  
Hand Pose Estimation ◽  
Sensing Technology ◽  
Hand Pose

scholarly journals Global hand pose estimation by multiple camera ellipse tracking

2008 ◽  
Vol 21 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Jorge Usabiaga ◽  
Ali Erol ◽  
George Bebis ◽  
Richard Boyle ◽  
Xander Twombly
Keyword(s):  
Pose Estimation ◽  
Multiple Camera ◽  
Hand Pose Estimation ◽  
Ellipse Tracking ◽  
Hand Pose

scholarly journals Global Hand Pose Estimation by Multiple Camera Ellipse Tracking

2006 ◽  
pp. 122-132 ◽  
Author(s):  
Jorge Usabiaga ◽  
Ali Erol ◽  
George Bebis ◽  
Richard Boyle ◽  
Xander Twombly
Keyword(s):  
Pose Estimation ◽  
Multiple Camera ◽  
Hand Pose Estimation ◽  
Ellipse Tracking ◽  
Hand Pose

scholarly journals Hand Pose Estimation in the Task of Egocentric Actions

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 10533-10547
Author(s):  
Marek Hruz ◽  
Jakub Kanis ◽  
Zdenek Krnoul
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals 3D Hand Pose Estimation via Graph-Based Reasoning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 35824-35833
Author(s):  
Jae-Hun Song ◽  
Suk-Ju Kang
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

scholarly journals Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1007
Author(s):  
Chi Xu ◽  
Yunkai Jiang ◽  
Jun Zhou ◽  
Yi Liu
Keyword(s):  
Gesture Recognition ◽  
Pose Estimation ◽  
Color Image ◽  
Recognition Performance ◽  
Recognition Task ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Estimation Task ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust 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.

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