Methods and algorithms of computer vision for automated processing of mineral rocks images

This paper contains development of methods and algorithms of computer vision for image recognition of mineral spices. It is described CASE-technology for automated systems development was used to create computer vision system for assessing the qualitative composition of mineral rocks. Methods and algorithms of computer vision of mineral rocks, in particular problems of the algorithm for automatic segmentation of colour images of ores, using the methods of cluster analysis are considered. Results of studies are demonstrated different colour spaces by k-means clustering. It was supposed the technique of pre-computing the values of the centroids. There is formulas translation metrics colour space HSV. The effectiveness of the proposed method lies in the automatic identification of interest objects on the total image, tuning parameters of the algorithm is a number that indicates the amount allocated to the segments.

1. Introduction

Petrography is the science that studies the material composition of the rocks. Unlike minerals, rocks are aggregates composed of different minerals [1]. Minerals are homogeneous in composition and structure of the rocks and ores. They are natural chemical compounds resulting from various geological processes. Historically minerals initially determined by color and shape.