D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of...

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Autores principales:	Ahmad Wali Satria Bahari Johan, Sekar Widyasari Putri, Granita Hajar, Ardian Yusuf Wicaksono
Formato:	article
Lenguaje:	ID
Publicado:	Universitas Udayana 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/6285930bda8f4715b3578b061f31fdf1
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spelling	oai:doaj.org-article:6285930bda8f4715b3578b061f31fdf12021-12-01T04:18:06ZD Modified KNN-LVQ for Stairs Down Detection Based on Digital Image2088-15412541-583210.24843/LKJITI.2021.v12.i03.p02https://doaj.org/article/6285930bda8f4715b3578b061f31fdf12021-11-01T00:00:00Zhttps://ojs.unud.ac.id/index.php/lontar/article/view/75212https://doaj.org/toc/2088-1541https://doaj.org/toc/2541-5832Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of their existence. Therefore we need a system that can identify the presence of stairs down. This study uses digital image processing technology in recognizing the stairs down. Digital images are used as input objects which will be extracted using the Gray Level Co-occurrence Matrix method and then classified using the KNN-LVQ hybrid method. The proposed algorithm is tested to determine the accuracy and computational speed obtained. Hybrid KNN-LVQ gets an accuracy of 95%. While the average computing speed obtained is 0.07248 (s).Ahmad Wali Satria Bahari JohanSekar Widyasari PutriGranita HajarArdian Yusuf WicaksonoUniversitas UdayanaarticleElectronic computers. Computer scienceQA75.5-76.95IDLontar Komputer, Vol 12, Iss 3, Pp 141-150 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Ahmad Wali Satria Bahari Johan Sekar Widyasari Putri Granita Hajar Ardian Yusuf Wicaksono D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image
description	Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of their existence. Therefore we need a system that can identify the presence of stairs down. This study uses digital image processing technology in recognizing the stairs down. Digital images are used as input objects which will be extracted using the Gray Level Co-occurrence Matrix method and then classified using the KNN-LVQ hybrid method. The proposed algorithm is tested to determine the accuracy and computational speed obtained. Hybrid KNN-LVQ gets an accuracy of 95%. While the average computing speed obtained is 0.07248 (s).
format	article
author	Ahmad Wali Satria Bahari Johan Sekar Widyasari Putri Granita Hajar Ardian Yusuf Wicaksono
author_facet	Ahmad Wali Satria Bahari Johan Sekar Widyasari Putri Granita Hajar Ardian Yusuf Wicaksono
author_sort	Ahmad Wali Satria Bahari Johan
title	D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image
title_short	D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image
title_full	D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image
title_fullStr	D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image
title_full_unstemmed	D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image
title_sort	d modified knn-lvq for stairs down detection based on digital image
publisher	Universitas Udayana
publishDate	2021
url	https://doaj.org/article/6285930bda8f4715b3578b061f31fdf1
work_keys_str_mv	AT ahmadwalisatriabaharijohan dmodifiedknnlvqforstairsdowndetectionbasedondigitalimage AT sekarwidyasariputri dmodifiedknnlvqforstairsdowndetectionbasedondigitalimage AT granitahajar dmodifiedknnlvqforstairsdowndetectionbasedondigitalimage AT ardianyusufwicaksono dmodifiedknnlvqforstairsdowndetectionbasedondigitalimage
_version_	1718405905383424000

D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

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