Dzięki algorytmom analizy obrazu komputery zyskują zdolność samodzielnego rozpoznawania tego, co widzą obiektywy kamer. W badaniach geologicznych ich szybkość uwalnia ludzi od konieczności mozolnego mierzenia ziaren mineralnych.

Image processing algorithms are enabling computers to learn to automatically recognize characteństics of what camera images portray. In geological research, speedy computers can relieve researchers from having to perform painstaking measurements on mineral grains.

In the experiments the lifetime, survival and feeding preferences of Sitona lineatus L. on the various broad bean cultivars were investigated. The measures of the injured disks were performed by means of automatic image analysis. This method led to reduction of time required for the measures. Based on these experiments it can be suggested that Hangdown Zielony was the most preferable and suitable cultivar for the S. lineatus adults from all tested broad bean cultivars. The largest injured disk surface, the longest life and the highest survival of Sitona weevils were recorded on this variety.

The petrographic composition of coal has a significant impact on its technological and sorption properties. That composition is most frequently determined by means of microscope quantitative analyses. Thus, aside from the purely scientific aspect, such measurements have an important practical application in the industrial usage of coal, as well as in issues related to the safety in underground mining facilities. The article discusses research aiming at analyzing the usefulness of selected parameters of a digital image description in the process of automatic identification of macerals of the inertinite group using neural networks. The description of the investigated images was based on statistical parameters determined on the basis of a histogram and co-occurrence matrix (Haralick parameters). Each of the studied macerals was described by means of a 20-element feature vector. An analysis of its principal components (PCA) was conducted, along with establishing the relationship between the number of the applied components and the effectiveness of the MLP network. Based on that, the optimum number of input variables for the investigated classification task was chosen, which resulted in reduction of the size of the network’s hidden layer. As part of the discussed research, the authors also analyzed the process of classification of macerals of the inertinite group using an algorithm based on a group of MLP networks, where each network possessed one output. As a result, average recognition effectiveness of 80.9% was obtained for a single MLP network, and of 93.6% for a group of neural networks. The obtained results indicate that it is possible to use the proposed methodology as a tool supporting microscopic analyses of coal.

The occurrence of faults in coal seams has an impact on the possibility of methane hazard. There are several methods for identifying tectonic faults, but they cannot be applied directly to solve dynamic hazard problems in coal mine. Thus, searching for appropriate methods, that can detect faults in regional and local scales is needed. In order to meet this need, the paper proposes a new measurement method of estimating changes to the coal structure, based on profilometry measurements (roughness analysis) and application of madogram functions. Based on examining coal samples from near fault zones it was shown that the proposed approach allows us to detect changes of the coal surface that appear as the distance to a tectonic fault gets shorter. The proposed method, due to its simplicity and speed of measurement, implies a potential for practical application in the process of detecting local tectonic dislocations in coal mines.