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Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, W. Pola 2, 35-959 Rzeszów, Poland
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Sensors 2019, 19(5), 1078; https://doi.org/10.3390/s19051078
Received: 18 December 2018 / Revised: 14 February 2019 / Accepted: 25 February 2019 / Published: 3 March 2019
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Abstract

The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images. View Full-Text
Keywords: hand posture recognition; fingerspelling; Polish finger alphabet; American finger alphabet; Kinect; point cloud; hidden Markov models hand posture recognition; fingerspelling; Polish finger alphabet; American finger alphabet; Kinect; point cloud; hidden Markov models
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Warchoł, D.; Kapuściński, T.; Wysocki, M. Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images. Sensors 2019, 19, 1078.

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