Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach
This paper presents solid waste bin level detection and classification using gray level co-occurrence matrix (GLCM) feature extraction methods. GLCM parameters, such as displacement, d, quantization, G, and the number of textural features, are investigated to determine the best parameter values of t...
| Publicado en: | Journal of Environmental Management Vol. 104; pp. 9 - 19 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Aug2012
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=75166490&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 75166490 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Aug2012 vid: 104 pid: 735 pub: Academic Press Inc. artinfo: ui: 75166490 10.1016/j.jenvman.2012.03.035 ppf: 9 ppct: 10 formats: tig: atl: Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach aug: au: Arebey, Maher Hannan, M.A. Begum, R.A. Basri, Hassan affil: Dept. of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia Institute for Environment & Development, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia Dept. of Civil & Structural Engineering, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia su: Solid waste management Bins Texture analysis (Image processing) Displacement (Mechanics) Grading (Commercial products) Perceptrons Statistical measurement sug: subj: Solid waste management Bins Texture analysis (Image processing) Displacement (Mechanics) Grading (Commercial products) Perceptrons Statistical measurement keyword: Classification and grading GLCM KNN MLP Solid waste monitoring and management Classification and grading GLCM KNN MLP Solid waste monitoring and management ab: This paper presents solid waste bin level detection and classification using gray level co-occurrence matrix (GLCM) feature extraction methods. GLCM parameters, such as displacement, d, quantization, G, and the number of textural features, are investigated to determine the best parameter values of the bin images. The parameter values and number of texture features are used to form the GLCM database. The most appropriate features collected from the GLCM are then used as inputs to the multi-layer perceptron (MLP) and the K-nearest neighbor (KNN) classifiers for bin image classification and grading. The classification and grading performance for DB1, DB2 and DB3 features were selected with both MLP and KNN classifiers. The results demonstrated that the KNN classifier, at KNN = 3, d = 1 and maximum G values, performs better than using the MLP classifier with the same database. Based on the results, this method has the potential to be used in solid waste bin level classification and grading to provide a robust solution for solid waste bin level detection, monitoring and management. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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