Abstract
This paper proposes a genetic-based hand posture identification system which consists of an efficient model fitting method and a labeling technique. The model fitting method consists of (1) finding the closed form inverse kinematics solution, (2) defining the alignment measure, and (3) developing a genetic-based dynamic posture fitting process. Different from the conventional computation intensive hand model fitting methods, we develop (1) an off-line training process which uses the inverse-kinematics to find the closed-form solution function, and (2) a fast on-line model-based hand'posture identification process. In the experiments, we will illustrate that our genetic-based hand posture identification system is effective and real-time implementable.
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© 1997 Springer-Verlag Berlin Heidelberg
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Lien, CC., Huang, CL. (1997). The model-based dynamic hand posture identification using genetic algorithm. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_185
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DOI: https://doi.org/10.1007/3-540-63930-6_185
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