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Efficacy comparison of clustering systems for limb detection
conference contribution
posted on 2014-01-01, 00:00 authored by Hussein Haggag, Mohammed Hossny, Sherif Haggag, Saeid Nahavandi, Douglas CreightonDouglas CreightonThis 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.
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Event
System of Systems Engineering. Conference (2014 : Adelaide, South Australia)Pagination
148 - 153Publisher
Institute of Electrical and Electronics Engineers Inc.Location
Adelade, South Australia)Publisher DOI
Start date
2014-06-09End date
2014-06-13ISBN-13
9781479952274Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright 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 EngineeringUsage metrics
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