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

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, we describe procedures employing proven active contours and evolutionary algorithms for recognizing points of interest in the images that may serve in determining various parameters and properties of analyzed objects. We use the evolutionary algorithms to optimize the parameters of the gradient vector flow field and the parameters affecting the geometrical properties of closed curve used to approximate the location and shape of object boundaries. We suppose that evolutionary algorithms can be used to find the desired global solution. As the computation of gradient vector flow field and also the evolution of active contour are computationally very expensive, we incorporate the GPU acceleration. In conclusion, we compare our approach with common numerical methods on real industrial images segmentation.