Copper ores are mineral formations of natural origin, the concentration of copper or copper compounds in which is sufficient for their commercial mining. Because of this, copper ores are on the list of minerals that are of strategic importance for the sustainable development of the economy and defense capability of Ukraine. In addition, due to the high thermal conductivity, copper and its alloys are widely used for the manufacture of various types of heat exchangers and radiators. The content of copper in ores usually ranges from 1 to 5%; ores containing less than 0.5% copper are unprofitable for processing at the current level of technology. Mining of copper is of strategic importance, as copper is the main material for the production of cable products and other conductive parts in the electrical industry. The efficiency of technological processes in the mining and industry during the production of copper depends on the quality of ores in relation to the content of minerals that contain copper. Copper ore can be considered a heterogeneous material consisting of two homogeneous phases, one phase is host rock, and the other is copper mineral. Modern image processing techniques have allowed as to automate the identification of minerals in ore samples. Automatic recognition and quantification of minerals using X-Ray tomography, scanning electronic microscope, light microscopy, is one of the most important problems in ore processing systems, as the amount of the minerals in the ore must be determined for further processing. A method of segmentation of colour images of sections of test samples of copper ore is proposed to estimate the percentage content of its components in the section of the tested samples. It is based on the use of chalcopyrite colour features in the HSV model. This colour-based segmentation method is proposed to exploit the average value and distribution of HSV colour components of chalcopyrite in an copper ore image. Segmentation parameters are configured. The experimental results of the segmentation of colour images of copper ore slices by the proposed method are analyzed. The effectiveness of the method is checked using synthesized test images. The method provides an absolute error less than 2.5%.

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