Distinguish Garnet Mineral from Pulmuddai Beach Sand Using Image Processing Techniques
| dc.contributor.author | Hirosh, D.M.H. | |
| dc.contributor.author | Darshana, J.V.A. | |
| dc.contributor.author | Doratiyawa, H.M.M.S. | |
| dc.contributor.author | Wilson, R.S.I. | |
| dc.contributor.author | Mehendran, Y. | |
| dc.contributor.author | Jaliya, R.G.C. | |
| dc.date.accessioned | 2019-04-06T07:23:52Z | |
| dc.date.available | 2019-04-06T07:23:52Z | |
| dc.date.issued | 2019-02 | |
| dc.description.abstract | Beach sand is one of the major minerals producing source in Sri Lanka. Pulmuddai Beach, rich in Ilmenite, Rutile and Zircon and Garnet and it is the largest mineral processing plant in Sri Lanka. It's a great necessity to explore high mineral localities for production. In the industrial level, the percentage of a specific mineral is calculated using a visual inspection through a microscope which is manual and time-consuming. The research introduces an innovative method to distinguish Garnet mineral from sand using image processing techniques. In this study, 1125 visible light RGB (Red, Green, Blue) images and 1125 Infrared (IR) images of beach sand were captured in a controlled light environment. RGB color composite images and IR images were analyzed separately to identify Garnet mineral from the gang and to calculate Garnet percentage. For the machine learning classification purpose, contrast, variance, mean, median, min, max, range, kurtosis, skewness, standard deviation and correlation were extracted from sand grains images. Then RGB, HSV (Hue, Saturation, Value) and RGBIR (Red, Green, Blue and Infrared) color models were used through a machine learning model. The highest accuracy of 63% of separation accuracy was given by the HSV color model. The accuracy could be increased by introducing more images to the machine learning process. The final model was built based on the HSV color model since it has the high accuracy of separation. Then the HSV model subjected to object counting model, area-based counting model and volume based counting model to identify the most suitable method for the percentage calculations. Among these three methods, an object counting model produced the more accuracy results with 57%. Thus, the HSV color model incorporates with object counting model produces the best combination to identify Garnet and calculate its percentage. | en_US |
| dc.identifier.isbn | 9789550481255 | |
| dc.identifier.uri | http://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/119/80.pdf?sequence=1&isAllowed=y | |
| dc.language.iso | en | en_US |
| dc.publisher | Uva Wellassa University of Sri Lanka | en_US |
| dc.subject | Computer Science | en_US |
| dc.subject | Information Science | en_US |
| dc.subject | Computing and Information Science | en_US |
| dc.title | Distinguish Garnet Mineral from Pulmuddai Beach Sand Using Image Processing Techniques | en_US |
| dc.title.alternative | International Research Conference 2019 | en_US |
| dc.type | Other | en_US |