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Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method

Recent years have witnessed renewed interest in developing skin segmentation approaches. Skin feature segmentation has been widely employed in different aspects of computer vision applications including face detection and hand gestures recognition systems. This is mostly due to the attractive charac...

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Main Authors: Thabet, Eman, Khalid, Fatimah, Sulaiman, Puteri Suhaiza, Yaakob, Razali
Format: Article
Language:English
Published: Hindawi Publishing Corporation 2017
Online Access:http://psasir.upm.edu.my/id/eprint/62321/1/Low%20cost%20skin%20segmentation%20scheme%20in%20videos%20using%20two%20alternative%20methods%20for%20dynamic%20hand%20gesture%20detection%20method.pdf
http://psasir.upm.edu.my/id/eprint/62321/
https://www.hindawi.com/journals/am/2017/7645189/
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spelling my.upm.eprints.623212019-10-31T04:04:57Z http://psasir.upm.edu.my/id/eprint/62321/ Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method Thabet, Eman Khalid, Fatimah Sulaiman, Puteri Suhaiza Yaakob, Razali Recent years have witnessed renewed interest in developing skin segmentation approaches. Skin feature segmentation has been widely employed in different aspects of computer vision applications including face detection and hand gestures recognition systems. This is mostly due to the attractive characteristics of skin colour and its effectiveness to object segmentation. On the contrary, there are certain challenges in using human skin colour as a feature to segment dynamic hand gesture, due to various illumination conditions, complicated environment, and computation time or real-time method. These challenges have led to the insufficiency of many of the skin color segmentation approaches. Therefore, to produce simple, effective, and cost efficient skin segmentation, this paper has proposed a skin segmentation scheme. This scheme includes two procedures for calculating generic threshold ranges in Cb-Cr colour space. The first procedure uses threshold values trained online from nose pixels of the face region. Meanwhile, the second procedure known as the offline training procedure uses thresholds trained out of skin samples and weighted equation. The experimental results showed that the proposed scheme achieved good performance in terms of efficiency and computation time. Hindawi Publishing Corporation 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/62321/1/Low%20cost%20skin%20segmentation%20scheme%20in%20videos%20using%20two%20alternative%20methods%20for%20dynamic%20hand%20gesture%20detection%20method.pdf Thabet, Eman and Khalid, Fatimah and Sulaiman, Puteri Suhaiza and Yaakob, Razali (2017) Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method. Advances in Multimedia, 2017. art. no. 7645189. pp. 1-10. ISSN 1687-5680; ESSN: 1687-5699 https://www.hindawi.com/journals/am/2017/7645189/ 10.1155/2017/7645189
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Recent years have witnessed renewed interest in developing skin segmentation approaches. Skin feature segmentation has been widely employed in different aspects of computer vision applications including face detection and hand gestures recognition systems. This is mostly due to the attractive characteristics of skin colour and its effectiveness to object segmentation. On the contrary, there are certain challenges in using human skin colour as a feature to segment dynamic hand gesture, due to various illumination conditions, complicated environment, and computation time or real-time method. These challenges have led to the insufficiency of many of the skin color segmentation approaches. Therefore, to produce simple, effective, and cost efficient skin segmentation, this paper has proposed a skin segmentation scheme. This scheme includes two procedures for calculating generic threshold ranges in Cb-Cr colour space. The first procedure uses threshold values trained online from nose pixels of the face region. Meanwhile, the second procedure known as the offline training procedure uses thresholds trained out of skin samples and weighted equation. The experimental results showed that the proposed scheme achieved good performance in terms of efficiency and computation time.
format Article
author Thabet, Eman
Khalid, Fatimah
Sulaiman, Puteri Suhaiza
Yaakob, Razali
spellingShingle Thabet, Eman
Khalid, Fatimah
Sulaiman, Puteri Suhaiza
Yaakob, Razali
Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method
author_facet Thabet, Eman
Khalid, Fatimah
Sulaiman, Puteri Suhaiza
Yaakob, Razali
author_sort Thabet, Eman
title Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method
title_short Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method
title_full Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method
title_fullStr Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method
title_full_unstemmed Low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method
title_sort low cost skin segmentation scheme in videos using two alternative methods for dynamic hand gesture detection method
publisher Hindawi Publishing Corporation
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/62321/1/Low%20cost%20skin%20segmentation%20scheme%20in%20videos%20using%20two%20alternative%20methods%20for%20dynamic%20hand%20gesture%20detection%20method.pdf
http://psasir.upm.edu.my/id/eprint/62321/
https://www.hindawi.com/journals/am/2017/7645189/
_version_ 1651869088109035520
score 13.211869

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