Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing
Estimation of the amount of fines in images of mineral particles using standard segmentation approaches is difficult. In this paper, an approach based on multivariate image analysis is presented for estimation of the amount of fines in particles on conveyor belts. The approach is based on two-level...
| Main Authors: | , |
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| Format: | Journal Article |
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International Association of Computer Science and Information Technology Press (IACSIT)
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/46851 |
| _version_ | 1848757674540269568 |
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| author | Amankwah, A. Aldrich, Chris |
| author_facet | Amankwah, A. Aldrich, Chris |
| author_sort | Amankwah, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Estimation of the amount of fines in images of mineral particles using standard segmentation approaches is difficult. In this paper, an approach based on multivariate image analysis is presented for estimation of the amount of fines in particles on conveyor belts. The approach is based on two-level wavelet decomposition and morphological image operations, followed by feature extraction from gray level co-occurrence matrices. These features could be used with a simple k nearest neighbour model to estimate the proportion of fines in particulate images. Experimental results with coal and iron ore particles show that the performance of the method can yield better results than those achievable with standard methods. |
| first_indexed | 2025-11-14T09:31:51Z |
| format | Journal Article |
| id | curtin-20.500.11937-46851 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:31:51Z |
| publishDate | 2011 |
| publisher | International Association of Computer Science and Information Technology Press (IACSIT) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-468512017-02-28T01:47:35Z Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing Amankwah, A. Aldrich, Chris Multivariate image analysis wavelets textural feature extraction particle size distribution Estimation of the amount of fines in images of mineral particles using standard segmentation approaches is difficult. In this paper, an approach based on multivariate image analysis is presented for estimation of the amount of fines in particles on conveyor belts. The approach is based on two-level wavelet decomposition and morphological image operations, followed by feature extraction from gray level co-occurrence matrices. These features could be used with a simple k nearest neighbour model to estimate the proportion of fines in particulate images. Experimental results with coal and iron ore particles show that the performance of the method can yield better results than those achievable with standard methods. 2011 Journal Article http://hdl.handle.net/20.500.11937/46851 International Association of Computer Science and Information Technology Press (IACSIT) restricted |
| spellingShingle | Multivariate image analysis wavelets textural feature extraction particle size distribution Amankwah, A. Aldrich, Chris Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing |
| title | Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing |
| title_full | Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing |
| title_fullStr | Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing |
| title_full_unstemmed | Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing |
| title_short | Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing |
| title_sort | estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing |
| topic | Multivariate image analysis wavelets textural feature extraction particle size distribution |
| url | http://hdl.handle.net/20.500.11937/46851 |