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A simple edges extraction method from complex digital images (EEMI)

This paper proposes a simple Edges Extraction Method from complex digital Images (EEMI). The proposed EEMI uses a simple image processing technique to detect edges of objects and regions inside complex scenarios of images. It highlights objects� edges by increasing their contrast levels and pixels�...

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Main Authors: Al-Ghaili A.M., Kasim H., Othman M., Hassan Z.
Other Authors: 26664381500
Format: Book Chapter
Published: Springer 2023
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id my.uniten.dspace-25014
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spelling my.uniten.dspace-250142023-05-29T15:30:23Z A simple edges extraction method from complex digital images (EEMI) Al-Ghaili A.M. Kasim H. Othman M. Hassan Z. 26664381500 57203863798 24824928800 13607947600 This paper proposes a simple Edges Extraction Method from complex digital Images (EEMI). The proposed EEMI uses a simple image processing technique to detect edges of objects and regions inside complex scenarios of images. It highlights objects� edges by increasing their contrast levels and pixels� intensities using special masks. EEMI mainly uses two simple masks one of which is used to detect vertical edges while the other one detects horizontal edges. Results have revealed that EEMI is a robust edge detector with inclined and complex background images. EEMI has been found simple and has simple complexity that helps reduce the computational time existent with other competitive methods. Results have confirmed that EEMI�s computation time could efficiently meet real-time requirements. EEMI has been compared to other competitive operators in terms of accuracy and computation time. � Springer Nature Singapore Pte Ltd. 2019. Final 2023-05-29T07:30:23Z 2023-05-29T07:30:23Z 2019 Book Chapter 10.1007/978-981-13-6031-2_16 2-s2.0-85066137689 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066137689&doi=10.1007%2f978-981-13-6031-2_16&partnerID=40&md5=f2c3f3fec52a825390b0c72ba02ca516 https://irepository.uniten.edu.my/handle/123456789/25014 67 215 224 Springer Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description This paper proposes a simple Edges Extraction Method from complex digital Images (EEMI). The proposed EEMI uses a simple image processing technique to detect edges of objects and regions inside complex scenarios of images. It highlights objects� edges by increasing their contrast levels and pixels� intensities using special masks. EEMI mainly uses two simple masks one of which is used to detect vertical edges while the other one detects horizontal edges. Results have revealed that EEMI is a robust edge detector with inclined and complex background images. EEMI has been found simple and has simple complexity that helps reduce the computational time existent with other competitive methods. Results have confirmed that EEMI�s computation time could efficiently meet real-time requirements. EEMI has been compared to other competitive operators in terms of accuracy and computation time. � Springer Nature Singapore Pte Ltd. 2019.
author2 26664381500
author_facet 26664381500
Al-Ghaili A.M.
Kasim H.
Othman M.
Hassan Z.
format Book Chapter
author Al-Ghaili A.M.
Kasim H.
Othman M.
Hassan Z.
spellingShingle Al-Ghaili A.M.
Kasim H.
Othman M.
Hassan Z.
A simple edges extraction method from complex digital images (EEMI)
author_sort Al-Ghaili A.M.
title A simple edges extraction method from complex digital images (EEMI)
title_short A simple edges extraction method from complex digital images (EEMI)
title_full A simple edges extraction method from complex digital images (EEMI)
title_fullStr A simple edges extraction method from complex digital images (EEMI)
title_full_unstemmed A simple edges extraction method from complex digital images (EEMI)
title_sort simple edges extraction method from complex digital images (eemi)
publisher Springer
publishDate 2023
_version_ 1806428437338914816
score 13.222552

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