000			03858nam a22004455c 4500
988			_aSpringer_Robotics_2020
999			_c115372 _d115372 _x1
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005			20230110040232.0
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008			190826s2020 si a o |||| 0|eng d
020			_a9789811397578
024	7		_a10.1007/978-981-13-9757-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ337.3 _b2020 EB
100	1		_aSaremi, Shahrzad _eautor _9671773
245	1	0	_aOptimisation algorithms for hand posture estimation _cby Shahrzad Saremi, Seyedali Mirjalili
250			_aFirst edition
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2020
300			_a1 recurso en línea (XV, 205 páginas) _b108 ilustraciones, 99 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aAlgorithms for Intelligent Systems _x2524-7565
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction to Hand Posture Estimation -- Literature Review of Hand Posture Estimation Techniques and Optimisation Algorithms -- A New 3D Hand Model, Hand Shape Optimization, and Evolutionary Population Dynamics for PSO and MOPSO -- Evaluating PSO and MOPSO equipped with Evolutionary Population Dynamics -- Hand shape optimisation for geometry-based models using EPD-based Particle Swarm Optimization -- Hand recovery for geometry-based models using EPD-based Particle Swarm Optimization -- Hand model estimation considering two objectives using EPD-based Multi-Objective Particle Swarm Optimization -- Conclusion.
520	3		_aThis 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.
650		7	_2embne _9145705 _aOptimización matemática
650		7	_2embne _aAlgoritmos computacionales _9151819
650		7	_2embne _aInteligencia artificial distribuida _9666577
700	1		_aMirjalili, Seyedali _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671160
776	0	8	_iPrinted edition: _z9789811397561
776	0	8	_iPrinted edition: _z9789811397585
776	0	8	_iPrinted edition: _z9789811397592
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-9757-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI