Abstract
The preceding chapter reviewed the literature and identified the current gaps in the main phases of hand posture estimation using models with simple components. It was discussed that the current hand models with simple components provide the least possible structural parameters (especially for changing the shape of a hand) to reduce the number of possible hand postures to search and computational cost of the objective function. It was observed that there should be more 3D hand models with simple components to provide a more flexibility and better estimation of both posture and handshape simultaneously. This chapter proposes a new hand model combining the best features of the current best models and a number of new components. The structural parameters of the proposed model allow better flexibility in terms of changing the shape.
Part of this chapter has been reprinted from Shahrzad Saremi, Seyedali Mirjalili, Andrew Lewis, Alan Wee Chung Liew, Jin Song Dong: Enhanced multi-objective particle swarm optimisation for estimating hand postures, Knowledge-Based Systems, Volume 158, pp. 175–195, 2018 with permission from Elsevier.
Part of this chapter has been reprinted from Shahrzad Saremi, Seyedali Mirjalili, Andrew Lewis: Vision-based hand posture estimation using a new hand model made of simple components, Optik, Volume 167, pp. 15–24, 2018 with permission from Elsevier.
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Saremi, S., Mirjalili, S. (2020). A New 3D Hand Model, Hand Shape Optimisation and Evolutionary Population Dynamics for PSO and MOPSO. In: Optimisation Algorithms for Hand Posture Estimation. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-9757-8_3
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DOI: https://doi.org/10.1007/978-981-13-9757-8_3
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