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Optimisation Algorithms for Hand Posture Estimation (Hardcover, 1st ed. 2020)
Loot Price: R3,112
Discovery Miles 31 120
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Optimisation Algorithms for Hand Posture Estimation (Hardcover, 1st ed. 2020)
Series: Algorithms for Intelligent Systems
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This book reviews the literature on hand posture estimation using
generative methods, identifying the current gaps, such as
sensitivity to hand shapes, sensitivity to a good initial posture,
difficult hand posture recovery in cases of loss in tracking, and
lack of addressing multiple objectives to maximize accuracy and
minimize computational cost. To fill these gaps, it proposes a new
3D hand model that combines the best features of the current 3D
hand models in the literature. It also discusses the development of
a hand shape optimization technique. To find the global optimum for
the single-objective problem formulated, it improves and applies
particle swarm optimization (PSO), one of the most highly regarded
optimization algorithms and one that is used successfully in both
science and industry. After formulating the problem,
multi-objective particle swarm optimization (MOPSO) is employed to
estimate the Pareto optimal front as the solution for this
bi-objective problem. The book also demonstrates the effectiveness
of the improved PSO in hand posture recovery in cases of tracking
loss. Lastly, the book examines the formulation of hand posture
estimation as a bi-objective problem for the first time. The case
studies included feature 50 hand postures extracted from five
standard datasets, and were used to benchmark the proposed 3D hand
model, hand shape optimization, and hand posture recovery.
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