The Carpathian Orava Basin is a tectonic structure filled with Neogene and Quaternary deposits superimposed on the collision zone between the ALCAPA and European plates. Tectonic features of the south-eastern margin of the Orava Basin and the adjoining part of the fore-arc Central Carpathian Palaeogene Basin were studied. Field observations of mesoscopic structures, analyses of digital elevation models and geological maps, supplemented with electrical resistivity tomography surveys were performed. Particular attention was paid to joint network analysis. The NE-SW-trending Krowiarki and Hruštinka-Biela Orava sinistral fault zones were recognized as key tectonic features that influenced the Orava Basin development. They constitute the north-eastern part of a larger Mur-Mürz-Žilina fault system that separates the Western Carpathians from the Eastern Alps. The interaction of these sinistral fault zones with the older tectonic structures of the collision zone caused the initiation and further development of the Orava Basin as a strike-slip-related basin. The Krowiarki Fault Zone subdivides areas with a different deformation pattern within the sediments of the Central Carpathian Palaeogene Basin and was active at least from the time of cessation of its sedimentation in the early Miocene. Comparison of structural data with the recent tectonic stress field, earthquake focal mechanisms and GPS measurements allows us to conclude that the Krowiarki Fault Zone shows a stable general pattern of tectonic activity for more than the last 20 myr and is presently still active.
The task of generating fast and accurate three-dimensional (3D) models of objects or scenes through a sequence of non-calibrated images is an active field of research. The recent development in algorithm optimization has resulted in many automatic solutions that can provide an accurate 3D model from texture-full objects. Structure-from-motion (SfM) is an image-based method that uses discriminative point-based feature identifier, such as SIFT, to locate feature points in the images. This method faces difficulties when presented with the objects made of homogenous or texture-less surfaces. To reconstruct such surfaces a well-known technique is to apply an artificial variety by covering the surface with a random texture pattern prior to the image capturing process. In this work, we designed three series of image patterns which are tested based on the contrast and density ratio which increases from the first to the last pattern within the same series. The performance of the patterns is evaluated by reconstructing the surface of a texture-less object and comparing it with the true data. Using the best-found patterns from the experiments, a 3D model of a Moai statue is reconstructed. The experimental results demonstrate that the density and structure of a pattern highly affects the quality of the reconstruction.