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Efficacy comparison of clustering systems for limb detection

Haggag,H, Hossny,M, Haggag,S, Nahavandi,S and Creighton,D 2014, Efficacy comparison of clustering systems for limb detection, in SOSE 2014 : The Socio-Technical Perspective : Proceedings of the 9th International Conference on System of Systems Engineering, Institute of Electrical and Electronics Engineers Inc., Piscataway, N. J, pp. 148-153, doi: 10.1109/SYSOSE.2014.6892479.

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Title Efficacy comparison of clustering systems for limb detection
Author(s) Haggag,H
Hossny,MORCID iD for Hossny,M orcid.org/0000-0002-1593-6296
Haggag,S
Nahavandi,S
Creighton,DORCID iD for Creighton,D orcid.org/0000-0002-9217-1231
Conference name System of Systems Engineering. Conference (2014 : Adelaide, South Australia)
Conference location Adelaide, South Australia
Conference dates 9-13 Jun. 2014
Title of proceedings SOSE 2014 : The Socio-Technical Perspective : Proceedings of the 9th International Conference on System of Systems Engineering
Editor(s) [Unknown]
Publication date 2014
Conference series International Conference on System of Systems Engineering (SOSE)
Start page 148
End page 153
Total pages 6
Publisher Institute of Electrical and Electronics Engineers Inc.
Place of publication Piscataway, N. J
Keyword(s) Depth Sensors
Hierarchical clustering
K-means
Microsoft Kinect
Summary 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.
ISBN 9781479952274
Language eng
DOI 10.1109/SYSOSE.2014.6892479
Field of Research 110999 Neurosciences not elsewhere classified
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Institute of Electrical and Electronics Engineers Inc.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070499

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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