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Sensors 2019, 19(8), 1805; https://doi.org/10.3390/s19081805

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

1
Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
2
Department of Control Systems and Instrumentation, VŠB-Technical University of Ostrava, 17. listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republic
*
Author to whom correspondence should be addressed.
Received: 17 March 2019 / Revised: 4 April 2019 / Accepted: 13 April 2019 / Published: 15 April 2019
(This article belongs to the Section Physical Sensors)
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Abstract

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. View Full-Text
Keywords: grain detection; seeded region growing segmentation; edge detection; feature extraction grain detection; seeded region growing segmentation; edge detection; feature extraction
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MDPI and ACS Style

Budzan, S.; Buchczik, D.; Pawełczyk, M.; Tůma, J. Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System. Sensors 2019, 19, 1805.

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