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Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System

This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT)...

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Detalles Bibliográficos
Autores principales: Budzan, Sebastian, Buchczik, Dariusz, Pawełczyk, Marek, Tůma, Jiří
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515149/
https://www.ncbi.nlm.nih.gov/pubmed/30991763
http://dx.doi.org/10.3390/s19081805
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author Budzan, Sebastian
Buchczik, Dariusz
Pawełczyk, Marek
Tůma, Jiří
author_facet Budzan, Sebastian
Buchczik, Dariusz
Pawełczyk, Marek
Tůma, Jiří
author_sort Budzan, Sebastian
collection PubMed
description This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack’s theory and a threshold selection method. Finally, all the detected grains are characterized by a set of shape features, which are used to classify the grains into separate fractions. The efficiency of the algorithm was evaluated with real copper ore samples of known granularity. The proposed method generates information on different granularity fractions at a time with a number of grain shape features.
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spelling pubmed-65151492019-05-30 Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System Budzan, Sebastian Buchczik, Dariusz Pawełczyk, Marek Tůma, Jiří Sensors (Basel) Article This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack’s theory and a threshold selection method. Finally, all the detected grains are characterized by a set of shape features, which are used to classify the grains into separate fractions. The efficiency of the algorithm was evaluated with real copper ore samples of known granularity. The proposed method generates information on different granularity fractions at a time with a number of grain shape features. MDPI 2019-04-15 /pmc/articles/PMC6515149/ /pubmed/30991763 http://dx.doi.org/10.3390/s19081805 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Budzan, Sebastian
Buchczik, Dariusz
Pawełczyk, Marek
Tůma, Jiří
Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System
title Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System
title_full Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System
title_fullStr Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System
title_full_unstemmed Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System
title_short Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System
title_sort combining segmentation and edge detection for efficient ore grain detection in an electromagnetic mill classification system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515149/
https://www.ncbi.nlm.nih.gov/pubmed/30991763
http://dx.doi.org/10.3390/s19081805
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