A Multichannel Convolutional Neural Network for Hand Posture Recognition
Proceedings of the 24th International Conference on Artificial Neural Networks (ICANN 2014),
Editors: Wermter, Stefan; Weber, Cornelius; Duch, W.; Honkela, T; Koprinkova-Hristova, P.; Magg, S.; Palm, G.; Villa, A.E.P.,
pages 403--410,
doi: 10.1007/978-3-319-11179-7_51
- Sep 2014
Associated documents :
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.
@InProceedings{BMWW14,
author = {Barros, Pablo and Magg, Sven and Weber, Cornelius and Wermter, Stefan},
title = {A Multichannel Convolutional Neural Network for Hand Posture Recognition},
booktitle = {Proceedings of the 24th International Conference on Artificial Neural Networks (ICANN 2014)},
editors = {Wermter, Stefan; Weber, Cornelius; Duch, W.; Honkela, T; Koprinkova-Hristova, P.; Magg, S.; Palm, G.; Villa, A.E.P.},
number = {},
volume = {},
pages = {403--410},
year = {2014},
month = {Sep},
publisher = {Springer Heidelberg},
doi = {10.1007/978-3-319-11179-7_51},
}