Abstract.
This paper proposes a new hand posture identification system which applies genetic algorithm to develop an efficient 3D hand-model-fitting method. The 3D hand-model-fitting method consists of (1) finding the closed-form inverse kinematics solution, (2) defining the alignment measure function for the wrist-fitting process, and (3) applying genetic algorithm to develop the dynamic hand posture identification process. In contrast to the conventional computationally intensive hand-model-fitting methods, we develop an off-line training process to find the closed-form inverse kinematics solution functions, and a fast model-based hand posture identification process. In the experiments, we will illustrate that our hand posture identification system is very effective.
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Received: 10 April 1997 / Accepted: 18 June 1998
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Lien, CC., Huang, CL. The model-based dynamic hand posture identification using genetic algorithm. Machine Vision and Applications 11, 107–121 (1999). https://doi.org/10.1007/s001380050095
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DOI: https://doi.org/10.1007/s001380050095