A hybrid method using kinect depth and color data stream for hand blobs segmentation
The recently developed depth sensors such as Kinect have provided new potential for human-computer interaction (HCI) and hand gesture are one of main parts in recent researches. Hand segmentation procedure is performed to acquire hand gesture from a captured image. In this paper, a method is produce...
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iium-584482018-01-04T02:22:05Z http://irep.iium.edu.my/58448/ A hybrid method using kinect depth and color data stream for hand blobs segmentation Karbasi, Mostafa Muhammad Yusof, Zulkefli Waqas, Ahmad Bhatti, Zeeshan Shah, Asadullah Koondhar, M.Y. Brohi, Imtiaz Ali T10.5 Communication of technical information The recently developed depth sensors such as Kinect have provided new potential for human-computer interaction (HCI) and hand gesture are one of main parts in recent researches. Hand segmentation procedure is performed to acquire hand gesture from a captured image. In this paper, a method is produced to segment hand blobs using both depth and color data frames. This method applies a body segmentation and an image threshold techniques to depth data frame using skeleton data and concurrently it uses SLIC super-pixel segmentation method to extract hand blobs from color data frame with the help of skeleton data. The proposed method has low computation time and shows significant results when basic assumption are fulfilled. Science International 2017-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/58448/1/mostapha-2017.pdf Karbasi, Mostafa and Muhammad Yusof, Zulkefli and Waqas, Ahmad and Bhatti, Zeeshan and Shah, Asadullah and Koondhar, M.Y. and Brohi, Imtiaz Ali (2017) A hybrid method using kinect depth and color data stream for hand blobs segmentation. Science International, 29 (3 (May-June)). pp. 515-519. ISSN 1013-5316 http://www.sci-int.com/pdf/636331278130296978.pdf |
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T10.5 Communication of technical information |
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T10.5 Communication of technical information Karbasi, Mostafa Muhammad Yusof, Zulkefli Waqas, Ahmad Bhatti, Zeeshan Shah, Asadullah Koondhar, M.Y. Brohi, Imtiaz Ali A hybrid method using kinect depth and color data stream for hand blobs segmentation |
description |
The recently developed depth sensors such as Kinect have provided new potential for human-computer interaction (HCI) and hand gesture are one of main parts in recent researches. Hand segmentation procedure is performed to acquire hand gesture from a captured image. In this paper, a method is produced to segment hand blobs using both depth and color data frames. This method applies a body segmentation and an image threshold techniques to depth data frame using skeleton data and concurrently it uses SLIC super-pixel segmentation method to extract hand blobs from color data frame with the help of skeleton data. The proposed method has low computation time and shows significant results when basic assumption are fulfilled. |
format |
Article |
author |
Karbasi, Mostafa Muhammad Yusof, Zulkefli Waqas, Ahmad Bhatti, Zeeshan Shah, Asadullah Koondhar, M.Y. Brohi, Imtiaz Ali |
author_facet |
Karbasi, Mostafa Muhammad Yusof, Zulkefli Waqas, Ahmad Bhatti, Zeeshan Shah, Asadullah Koondhar, M.Y. Brohi, Imtiaz Ali |
author_sort |
Karbasi, Mostafa |
title |
A hybrid method using kinect depth and color data stream for hand blobs segmentation |
title_short |
A hybrid method using kinect depth and color data stream for hand blobs segmentation |
title_full |
A hybrid method using kinect depth and color data stream for hand blobs segmentation |
title_fullStr |
A hybrid method using kinect depth and color data stream for hand blobs segmentation |
title_full_unstemmed |
A hybrid method using kinect depth and color data stream for hand blobs segmentation |
title_sort |
hybrid method using kinect depth and color data stream for hand blobs segmentation |
publisher |
Science International |
publishDate |
2017 |
url |
http://irep.iium.edu.my/58448/ http://irep.iium.edu.my/58448/ http://irep.iium.edu.my/58448/1/mostapha-2017.pdf |
first_indexed |
2023-09-18T21:22:39Z |
last_indexed |
2023-09-18T21:22:39Z |
_version_ |
1777411984422076416 |