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Titlebook: Optimisation Algorithms for Hand Posture Estimation; Shahrzad Saremi,Seyedali Mirjalili Book 2020 The Editor(s) (if applicable) and The Au

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發(fā)表于 2025-3-21 19:01:27 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Optimisation Algorithms for Hand Posture Estimation
編輯Shahrzad Saremi,Seyedali Mirjalili
視頻videohttp://file.papertrans.cn/704/703111/703111.mp4
概述Proposes a new 3D hand model with simple shapes and low computational complexity.Demonstrates improved particle swarm optimization and multi-objective particle swarm optimization.Based on case studies
叢書名稱Algorithms for Intelligent Systems
圖書封面Titlebook: Optimisation Algorithms for Hand Posture Estimation;  Shahrzad Saremi,Seyedali Mirjalili Book 2020 The Editor(s) (if applicable) and The Au
描述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 alsodemonstrates 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 fo
出版日期Book 2020
關(guān)鍵詞Hand Posture Estimation; Particle Swarm Optimization; Multi-objective Particle Swarm Optimization; Hand
版次1
doihttps://doi.org/10.1007/978-981-13-9757-8
isbn_softcover978-981-13-9759-2
isbn_ebook978-981-13-9757-8Series ISSN 2524-7565 Series E-ISSN 2524-7573
issn_series 2524-7565
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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發(fā)表于 2025-3-21 22:47:56 | 只看該作者
Evaluating PSO and MOPSO Equipped with Evolutionary Population Dynamics,istics and difficulties are employed to efficiently benchmark the performance of the proposed PSO.EPD and MOPSO.EPD algorithms. The results are collected and presented quantitatively and qualitatively.
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Introduction to Hand Posture Estimation,Computers are an essential tool for the Information Age in the modern world. They are, essentially, a tool to aid the human mind. To be effective, information needs to get to and from the human mind. For much of the evolution of computers, this was text-based, using simple keyboards and monitors.
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Shahrzad Saremi,Seyedali MirjaliliProposes a new 3D hand model with simple shapes and low computational complexity.Demonstrates improved particle swarm optimization and multi-objective particle swarm optimization.Based on case studies
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Optimisation Algorithms for Hand Posture Estimation978-981-13-9757-8Series ISSN 2524-7565 Series E-ISSN 2524-7573
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