Abstract
Three-dimensional (3D) visualization of medical images is an important technology for efficiently conducting a surgery. However, efficient review of 3D anatomical models is required to maintain sterile field conditions. An operation using touchless interface for gesture recognition is one of the review methods. Real-time hand gesture application for supporting a surgery requires a robust recognition of various gestures. This study proposes a robust hand gesture recognition using multimodal deep learning to perform recognition using color and depth images. We evaluated the recognition accuracy of 25 different gestures and compared its recognition accuracy with conventional recognition methods. Resultantly, it was found that the proposed system achieves better real-time robust recognition than conventional methods.
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Acknowledgment
Authors would like to thank Dr. M. Kaibori of KANSAI Medical University for providing medical images and advice on surgical support systems. This work is supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant Nos. 18H03267, 18K11454, 17H00754, 17K00420; and in part by Zhejiang Lab Program under the Grant No. 2018DG0ZX01.
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Furusawa, K., Liu, J., Tsujinaga, S., Tateyama, T., Iwamoto, Y., Chen, YW. (2020). Robust Hand Gesture Recognition Using Multimodal Deep Learning for Touchless Visualization of 3D Medical Images. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_65
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DOI: https://doi.org/10.1007/978-3-030-32456-8_65
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