Abstract
This paper presents a hand shape recognition system using an active contour model (ACM) and applies it to an HCI to control a mobile robot. For the recognition of hand shapes, the technique should be developed to accurately track variously changing hands in real-time. For this, we develop a mean-shift embedded active contour (MEAC) which can improve the convergence speed and the tracking accurracy than the standard ACM. The proposed recognition system consists of four modules: a hand detector, a hand tracker, a hand shape recognizer and a robot controller. The hand detector locates a skin color region with a specific shape as a hand in the first frame. Thereafter, the detected region is accurately tracked through the whole video sequence by the hand tracker using a MEAC, and its shape is recognized using Hue moments. To assess the validity of the proposed system, we tested the proposed system to a walking robot, RCB-1. The experimental results show the effectiveness of the proposed system.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, E.Y. (2006). Hand Shape Recognition Using a Mean-Shift Embedded Active Contour (MEAC). In: Pan, Z., Cheok, A., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds) Advances in Artificial Reality and Tele-Existence. ICAT 2006. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941354_138
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DOI: https://doi.org/10.1007/11941354_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49776-9
Online ISBN: 978-3-540-49779-0
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