Estimation of copper concentrate grade based on color features and least-squares support vector regression

• Autorzy: Ren C. , Yang J. , Liang C.

Identyfikatory

DOI

10.5277/ppmp150115

• Języki publikacji: EN

Abstrakty

EN

In this paper, a new method based on color features of microscopic image and least-squares support vector regression model (LS-SVR) is proposed for indirect measurement of copper concentrate grade. Red, green and blue (RGB), hue and color vector angle were extracted from color microscopic images of a copper concentrate sample and selected for the comparison. Three different estimation models based on LS-SVR were developed using RGB, hue, and color vector angle, respectively. A comparison of three models was carried out through a validation test. The best model was obtained for the hue giving a running time of 30.243 ms, root mean square error of 0.8644 and correlation coefficient value of 0.9997. The results indicated that the copper concentrate grade could be estimated by the LS-SVR model using the hue as input parameter with a satisfactory accuracy.

Słowa kluczowe

EN

concentrate grade; copper concentrate; LS-SVR; color features; microscopic image

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2015
• Tom: Vol. 51, iss. 1
• Strony: 163--172
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Ren C.

• College of Information Management, Dezhou University, Dezhou, 250323, China

autor

Yang J.

• National Engineering Research Center of Coal Preparation and Purification, China University of Mining and Technology, Xuzhou, 221116, China

autor

Liang C.

• National Engineering Research Center of Coal Preparation and Purification, China University of Mining and Technology, Xuzhou, 221116, China

Bibliografia

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