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Efficacy comparison of clustering systems for limb detection

Version 2 2024-06-04, 02:17
Version 1 2015-03-10, 15:18
conference contribution
posted on 2024-06-04, 02:17 authored by H Haggag, M Hossny, S Haggag, S Nahavandi, Douglas CreightonDouglas Creighton
This paper presents a comparison of applying different clustering algorithms on a point cloud constructed from the depth maps captured by a RGBD camera such as Microsoft Kinect. The depth sensor is capable of returning images, where each pixel represents the distance to its corresponding point not the RGB data. This is considered as the real novelty of the RGBD camera in computer vision compared to the common video-based and stereo-based products. Depth sensors captures depth data without using markers, 2D to 3D-transition or determining feature points. The captured depth map then cluster the 3D depth points into different clusters to determine the different limbs of the human-body. The 3D points clustering is achieved by different clustering techniques. Our Experiments show good performance and results in using clustering to determine different human-body limbs.

History

Pagination

148-153

Location

Adelade, South Australia)

Start date

2014-06-09

End date

2014-06-13

ISBN-13

9781479952274

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2014, Institute of Electrical and Electronics Engineers Inc.

Editor/Contributor(s)

[Unknown]

Title of proceedings

SOSE 2014 : The Socio-Technical Perspective : Proceedings of the 9th International Conference on System of Systems Engineering

Event

System of Systems Engineering. Conference (2014 : Adelaide, South Australia)

Publisher

Institute of Electrical and Electronics Engineers Inc.

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