Let’s consider two objectives when estimating hand postures

Shahrzad Saremi; Seyedali Mirjalili; Andrew Lewis; Alan Wee Chung Liew

doi:10.1007/978-3-319-63004-5_10

Let’s consider two objectives when estimating hand postures

Shahrzad Saremi
, Seyedali Mirjalili
, Andrew Lewis
, Alan Wee Chung Liew

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hand posture estimation is an important step in hand gesture detection. It refers to the process of modeling hand in computer to accurately represent the actual hand obtained from an acquisition device. In the literature, several objective functions (mostly based on silhouette or point cloud) have been used to formulate and solve the problem of hand posture estimation as a minimisation problem using stochastic or deterministic algorithms. The main challenge is that the objective function is computationally expensive. In the case of using point clouds, decreasing the number of points results in a better computational cost, but it decreases the accuracy of hand posture estimation. We argue in this paper that hand posture estimation is a bi-objective problem with two conflicting objectives: minimising the error versus minimising the number of points in the point cloud. As an early effort, this paper first formulates hand posture estimation as a bi-objective optimisation problem and then approximates its true Pareto optimal front with an improved Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm. The proposed algorithm is used to determine the Pareto optimal front for 16 hand postures and compared with the original MOPSO. The results proved that the objectives are in conflict and the improved MOPSO outperforms the original algorithm when solving this problem.

Original language	English
Title of host publication	AI 2017
Subtitle of host publication	Advances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings
Editors	Wei Peng, Damminda Alahakoon, Xiaodong Li
Publisher	Springer Verlag
Pages	119-130
Number of pages	12
ISBN (Print)	9783319630038
DOIs	https://doi.org/10.1007/978-3-319-63004-5_10
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	30th Australasian Joint Conference on Artificial Intelligence, AI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 20 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10400 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th Australasian Joint Conference on Artificial Intelligence, AI 2017
Country/Territory	Australia
City	Melbourne
Period	19/08/17 → 20/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

Keywords

Hand posture estimation
MOPSO
Mulit-objective particle swarm optimisation
Multi-objective optimisation

Access to Document

10.1007/978-3-319-63004-5_10

Cite this

Saremi, S., Mirjalili, S., Lewis, A., & Liew, A. W. C. (2017). Let’s consider two objectives when estimating hand postures. In W. Peng, D. Alahakoon, & X. Li (Eds.), AI 2017: Advances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings (pp. 119-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10400 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-63004-5_10

Saremi, Shahrzad ; Mirjalili, Seyedali ; Lewis, Andrew et al. / Let’s consider two objectives when estimating hand postures. AI 2017: Advances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings. editor / Wei Peng ; Damminda Alahakoon ; Xiaodong Li. Springer Verlag, 2017. pp. 119-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5e1a5033e38b49dca687d62ebd6ccd73,

title = "Let{\textquoteright}s consider two objectives when estimating hand postures",

abstract = "Hand posture estimation is an important step in hand gesture detection. It refers to the process of modeling hand in computer to accurately represent the actual hand obtained from an acquisition device. In the literature, several objective functions (mostly based on silhouette or point cloud) have been used to formulate and solve the problem of hand posture estimation as a minimisation problem using stochastic or deterministic algorithms. The main challenge is that the objective function is computationally expensive. In the case of using point clouds, decreasing the number of points results in a better computational cost, but it decreases the accuracy of hand posture estimation. We argue in this paper that hand posture estimation is a bi-objective problem with two conflicting objectives: minimising the error versus minimising the number of points in the point cloud. As an early effort, this paper first formulates hand posture estimation as a bi-objective optimisation problem and then approximates its true Pareto optimal front with an improved Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm. The proposed algorithm is used to determine the Pareto optimal front for 16 hand postures and compared with the original MOPSO. The results proved that the objectives are in conflict and the improved MOPSO outperforms the original algorithm when solving this problem.",

keywords = "Hand posture estimation, MOPSO, Mulit-objective particle swarm optimisation, Multi-objective optimisation",

author = "Shahrzad Saremi and Seyedali Mirjalili and Andrew Lewis and Liew, \{Alan Wee Chung\}",

year = "2017",

month = jan,

day = "1",

doi = "10.1007/978-3-319-63004-5\_10",

language = "English",

isbn = "9783319630038",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "119--130",

editor = "Wei Peng and Damminda Alahakoon and Xiaodong Li",

booktitle = "AI 2017",

note = "30th Australasian Joint Conference on Artificial Intelligence, AI 2017 ; Conference date: 19-08-2017 Through 20-08-2017",

}

Saremi, S, Mirjalili, S, Lewis, A & Liew, AWC 2017, Let’s consider two objectives when estimating hand postures. in W Peng, D Alahakoon & X Li (eds), AI 2017: Advances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10400 LNAI, Springer Verlag, pp. 119-130, 30th Australasian Joint Conference on Artificial Intelligence, AI 2017, Melbourne, Australia, 19/08/17. https://doi.org/10.1007/978-3-319-63004-5_10

Let’s consider two objectives when estimating hand postures. / Saremi, Shahrzad; Mirjalili, Seyedali; Lewis, Andrew et al.
AI 2017: Advances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings. ed. / Wei Peng; Damminda Alahakoon; Xiaodong Li. Springer Verlag, 2017. p. 119-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10400 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Let’s consider two objectives when estimating hand postures

AU - Saremi, Shahrzad

AU - Mirjalili, Seyedali

AU - Lewis, Andrew

AU - Liew, Alan Wee Chung

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Hand posture estimation is an important step in hand gesture detection. It refers to the process of modeling hand in computer to accurately represent the actual hand obtained from an acquisition device. In the literature, several objective functions (mostly based on silhouette or point cloud) have been used to formulate and solve the problem of hand posture estimation as a minimisation problem using stochastic or deterministic algorithms. The main challenge is that the objective function is computationally expensive. In the case of using point clouds, decreasing the number of points results in a better computational cost, but it decreases the accuracy of hand posture estimation. We argue in this paper that hand posture estimation is a bi-objective problem with two conflicting objectives: minimising the error versus minimising the number of points in the point cloud. As an early effort, this paper first formulates hand posture estimation as a bi-objective optimisation problem and then approximates its true Pareto optimal front with an improved Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm. The proposed algorithm is used to determine the Pareto optimal front for 16 hand postures and compared with the original MOPSO. The results proved that the objectives are in conflict and the improved MOPSO outperforms the original algorithm when solving this problem.

AB - Hand posture estimation is an important step in hand gesture detection. It refers to the process of modeling hand in computer to accurately represent the actual hand obtained from an acquisition device. In the literature, several objective functions (mostly based on silhouette or point cloud) have been used to formulate and solve the problem of hand posture estimation as a minimisation problem using stochastic or deterministic algorithms. The main challenge is that the objective function is computationally expensive. In the case of using point clouds, decreasing the number of points results in a better computational cost, but it decreases the accuracy of hand posture estimation. We argue in this paper that hand posture estimation is a bi-objective problem with two conflicting objectives: minimising the error versus minimising the number of points in the point cloud. As an early effort, this paper first formulates hand posture estimation as a bi-objective optimisation problem and then approximates its true Pareto optimal front with an improved Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm. The proposed algorithm is used to determine the Pareto optimal front for 16 hand postures and compared with the original MOPSO. The results proved that the objectives are in conflict and the improved MOPSO outperforms the original algorithm when solving this problem.

KW - Hand posture estimation

KW - MOPSO

KW - Mulit-objective particle swarm optimisation

KW - Multi-objective optimisation

UR - https://www.scopus.com/pages/publications/85026729226

U2 - 10.1007/978-3-319-63004-5_10

DO - 10.1007/978-3-319-63004-5_10

M3 - Conference contribution

AN - SCOPUS:85026729226

SN - 9783319630038

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 119

EP - 130

BT - AI 2017

A2 - Peng, Wei

A2 - Alahakoon, Damminda

A2 - Li, Xiaodong

PB - Springer Verlag

T2 - 30th Australasian Joint Conference on Artificial Intelligence, AI 2017

Y2 - 19 August 2017 through 20 August 2017

ER -

Saremi S, Mirjalili S, Lewis A, Liew AWC. Let’s consider two objectives when estimating hand postures. In Peng W, Alahakoon D, Li X, editors, AI 2017: Advances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings. Springer Verlag. 2017. p. 119-130. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-63004-5_10

Let’s consider two objectives when estimating hand postures

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this