This thesis deals with image analysis methods which can be exploited in exoscopic analysis of sedimentary grains, specifically for the purpose of distinguishing between geomorphologic geneses which influenced a form of sedimentary grains. The images of sedimentary grains were acquired by a scanning electron microscope. The main contribution is the proposal of multiple methods that can significantly automate the exoscopic analysis. These methods cover the automatic segmentation of grains in image, the automatic analysis of roundness of 2D grain projection and the classification of geomorphologic geneses according to the grain surface structure. In the section concerning the automatic segmentation, a segmentation method enabling an easy subsequent manual result correction was proposed. This method is based on the split-and-merge approach. The individual steps the procedure were designed to exploit specific properties of sedimentary grain images in order to obtain the best segmentation results. In the section concerning the automatic roundness analysis of 2D projection of sedimentary grains, an influence of pixel resolution on a result roundness value was evaluated. Further, a minimal number of grains, which is necessary to analyze in order to reliably compare a pair of geomorphological geneses, was investigated. For the determination of this number, a method was proposed and experimentally verified. In the section of automatic analysis of sedimentary grain surface structure, a method for classification of geomorphologic geneses was proposed. The method utilizes low-level texture features which describes individual images of sedimentary grains. A model of geomorphological genesis is constituted of a set of histograms representing occurrences of different configurations of low-level texture features. The methods proposed in the thesis were tested and evaluated based on a database, which consists of sedimentary grain samples from 4 different geomorphological geneses (eolic, glacial, slope and volcanic).