Natural communication between humans involves hand gestures, which has an impact on research in human-robot interaction. In a real-world scenario, understanding human gestures by a robot is hard due to several challenges like hand segmentation. To recognize hand postures this paper proposes a novel convolutional implementation. The model is able to recognize hand postures recorded by a robot camera in real-time, in a real-world application scenario. The proposed model was also evaluated with a benchmark database and showed better results than the ones reported in the benchmark paper. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Barros, P., Magg, S., Weber, C., & Wermter, S. (2014). A multichannel convolutional neural network for hand posture recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 403–410). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_51
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