東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Optimisation algorithms for hand pos...

Saremi, Shahrzad.

FindBook

Google Book

Amazon

博客來

Optimisation algorithms for hand posture estimation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimisation algorithms for hand posture estimation/ by Shahrzad Saremi, Seyedali Mirjalili.
作者:	Saremi, Shahrzad.
其他作者:	Mirjalili, Seyedali.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xv, 205 p. :ill., digital ;24 cm.
內容註:	Introduction 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.
Contained By:	Springer Nature eBook
標題:	Swarm intelligence. -
電子資源:	https://doi.org/10.1007/978-981-13-9757-8
ISBN:	9789811397578

Optimisation algorithms for hand posture estimation
Saremi, Shahrzad.

Optimisation algorithms for hand posture estimation[electronic resource] /by Shahrzad Saremi, Seyedali Mirjalili. - Singapore :Springer Singapore :2020. - xv, 205 p. :ill., digital ;24 cm. - Algorithms for intelligent systems,2524-7565. - Algorithms for intelligent systems..

Introduction 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.

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.

ISBN: 9789811397578

Standard No.: 10.1007/978-981-13-9757-8doiSubjects--Topical Terms:

577800
Swarm intelligence.

LC Class. No.: Q337.3 / .S37 2020

Dewey Class. No.: 006.3824

Optimisation algorithms for hand posture estimation
LDR:03110nmm a2200337 a 4500 001 2258300
003 DE-He213
005 20200703082740.0
006 m d
007 cr nn 008maaau
008 220420s2020 si s 0 eng d
020 $a 9789811397578 $q (electronic bk.)
020 $a 9789811397561 $q (paper)
024 7 $a 10.1007/978-981-13-9757-8 $2 doi
035 $a 978-981-13-9757-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q337.3 $b .S37 2020
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3824 $2 23
090 $a Q337.3 $b .S244 2020
100 1 $a Saremi, Shahrzad. $3 3530224
245 1 0 $a Optimisation algorithms for hand posture estimation $h [electronic resource] / $c by Shahrzad Saremi, Seyedali Mirjalili.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xv, 205 p. : $b ill., digital ; $c 24 cm.
490 1 $a Algorithms for intelligent systems, $x 2524-7565
505 0 $a Introduction 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 $a 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.
650 0 $a Swarm intelligence. $3 577800
650 0 $a Mathematical optimization. $3 517763
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Optimization. $3 891104
700 1 $a Mirjalili, Seyedali. $3 3378331
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Algorithms for intelligent systems. $3 3443393
856 4 0 $u https://doi.org/10.1007/978-981-13-9757-8
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)