Fingertip detection using histogram of gradients and support vector machine

One important application in computer vision is detection of objects. This paper discusses detection of fingertips by using Histogram of Gradients (HOG) as the feature descriptor and Support Vector Machines (SVM) as the classifier. The SVM is trained to produce a classifier that is able to distingui...

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Main Authors:	Sophian, Ali, Awang Za’aba, Dayang Qurratu’aini
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science 2017
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://irep.iium.edu.my/60111/ http://irep.iium.edu.my/60111/ http://irep.iium.edu.my/60111/ http://irep.iium.edu.my/60111/1/60111_Fingertip%20Detection%20Using%20Histogram.pdf http://irep.iium.edu.my/60111/7/Fingertip%20detection%20using%20histogram%20of%20gradients%20and%20support%20vector%20machine.pdf

id	iium-60111
recordtype	eprints
spelling	iium-601112018-04-25T09:08:55Z http://irep.iium.edu.my/60111/ Fingertip detection using histogram of gradients and support vector machine Sophian, Ali Awang Za’aba, Dayang Qurratu’aini QA75 Electronic computers. Computer science QA76 Computer software One important application in computer vision is detection of objects. This paper discusses detection of fingertips by using Histogram of Gradients (HOG) as the feature descriptor and Support Vector Machines (SVM) as the classifier. The SVM is trained to produce a classifier that is able to distinguish whether an image contains a fingertip or not. A total of 4200 images were collected by using a commercial-grade webcam, consisting of 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our work evaluates the performance of the fingertip detection and the effects of the cell’s size of the HOG and the number of the training data have been studied. It has been found that as expected, the performance of the detection is improved as the number of training data is increased. Additionally, it has also been observed that the 10 x 10 size gives the best results in terms of accuracy in the detection. The highest classification accuracy obtained was less than 90%, which is thought mainly due to the changing orientation of the fingertip and quality of the images. Institute of Advanced Engineering and Science 2017-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/60111/1/60111_Fingertip%20Detection%20Using%20Histogram.pdf application/pdf en http://irep.iium.edu.my/60111/7/Fingertip%20detection%20using%20histogram%20of%20gradients%20and%20support%20vector%20machine.pdf Sophian, Ali and Awang Za’aba, Dayang Qurratu’aini (2017) Fingertip detection using histogram of gradients and support vector machine. Indonesian Journal of Electrical Engineering and Computer Science, 8 (2). pp. 482-486. ISSN 2502-4752 E-ISSN 2502-4760 http://www.iaesjournal.com/online/index.php/IJEECS/article/view/17162 10.11591/ijeecs.v8.i2.pp482-486
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Sophian, Ali Awang Za’aba, Dayang Qurratu’aini Fingertip detection using histogram of gradients and support vector machine
description	One important application in computer vision is detection of objects. This paper discusses detection of fingertips by using Histogram of Gradients (HOG) as the feature descriptor and Support Vector Machines (SVM) as the classifier. The SVM is trained to produce a classifier that is able to distinguish whether an image contains a fingertip or not. A total of 4200 images were collected by using a commercial-grade webcam, consisting of 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our work evaluates the performance of the fingertip detection and the effects of the cell’s size of the HOG and the number of the training data have been studied. It has been found that as expected, the performance of the detection is improved as the number of training data is increased. Additionally, it has also been observed that the 10 x 10 size gives the best results in terms of accuracy in the detection. The highest classification accuracy obtained was less than 90%, which is thought mainly due to the changing orientation of the fingertip and quality of the images.
format	Article
author	Sophian, Ali Awang Za’aba, Dayang Qurratu’aini
author_facet	Sophian, Ali Awang Za’aba, Dayang Qurratu’aini
author_sort	Sophian, Ali
title	Fingertip detection using histogram of gradients and support vector machine
title_short	Fingertip detection using histogram of gradients and support vector machine
title_full	Fingertip detection using histogram of gradients and support vector machine
title_fullStr	Fingertip detection using histogram of gradients and support vector machine
title_full_unstemmed	Fingertip detection using histogram of gradients and support vector machine
title_sort	fingertip detection using histogram of gradients and support vector machine
publisher	Institute of Advanced Engineering and Science
publishDate	2017
url	http://irep.iium.edu.my/60111/ http://irep.iium.edu.my/60111/ http://irep.iium.edu.my/60111/ http://irep.iium.edu.my/60111/1/60111_Fingertip%20Detection%20Using%20Histogram.pdf http://irep.iium.edu.my/60111/7/Fingertip%20detection%20using%20histogram%20of%20gradients%20and%20support%20vector%20machine.pdf
first_indexed	2023-09-18T21:25:12Z
last_indexed	2023-09-18T21:25:12Z
_version_	1777412144941236224

Fingertip detection using histogram of gradients and support vector machine

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