Development of methods for the processing of mining images using genetic algorithms

Abstract

In this paper we describe the extension of system FOTOM capabilities with respect to segmentation of specific mining images.We focus on methods that are inherently resistant against noise present in experimental pit at VSB Technical University. Here, wedescribe procedures employing proven active contours and evolutionary algorithms for recognizing points of interest in the imagesthat may serve in determining various parameters and properties of analyzed objects. We use the evolutionary algorithms to optimizethe parameters of the gradient vector flow field and the parameters affecting the geometrical properties of closed curve used toapproximate the location and shape of object boundaries. We suppose that evolutionary algorithms can be used to find the desiredglobal solution. As the computation of gradient vector flow field and also the evolution of active contour are computationally veryexpensive, we incorporate the GPU acceleration. In conclusion, we compare our approach with common numerical methods on realindustrial images segmentation.

Keywords

• mining image
• image segmentation
• active contour
• GVF
• SOMA