Automatic methods for constructing navigation routes do not fully meet all requirements. The aim of this study was to modify the methodology for generating indoor navigation models based on the Medial Axis Transformation (MAT) algorithm. The simplified method for generating corridor axes relies on the Node-Relation Structure (NRS) methodology. The axis of the modeled structure (corridor) is then determined based on the points of the middle lines intersecting the structure (polygon). The proposed solution involves a modified approach to the segmentation of corridor space. Traditional approaches rely on algorithms for generating Triangulated Irregular Networks (TINs) by Delaunay triangulation or algorithms for generating Thiessen polygons known as Voronoi diagrams (VDs). In this study, both algorithms were used in the segmentation process. The edges of TINs intersected structures. Selected midpoints on TIN edges, which were located in the central part of the structure, were used to generate VDs. Corridor structures were segmented by polygon VDs. The identifiers or structure nodes were the midpoints on the TIN edges rather than the calculated centroids. The generated routes were not zigzag lines, and they approximated natural paths. The main advantage of the proposed solution is its simplicity, which can be attributed to the use of standard tools for processing spatial data in a geographic information system.
Indoor route networks models are created for use in navigation. They may be built manually, but it is better to generate them automatically, based on the building floor plans. Research has been conducted in this field in many research centers. The authors undertook to develop their own methodology for generating navigation networks, using topological neighborhood relations and semantic data. The research project focuses on one floor in a building, which consists of rooms and an expanded corridor with an obstacle in the form of an open space between the floors. The first stage of the project consisted in the segmentation of the corridor space to improve its resolution. The objective of the conducted research was to select special points (five suggestions) for the segmentation. As a result, five different segmentations of the corridor space were obtained. The aim of the second stage was to automatically generate five navigation network models. The graphically presented results have been verified against the routes generated between the selected points in the building plan. A comparison of the results with other solutions shows that the routes generated in the presented methodology are more straight-line and less zigzagging.
CC-BY 4.0 License requiring that the original work has been properly cited.in stratum. Possibilities of using graph databases for storing geological data were checked. This study focuses on proposing a graph representation of geological stratum. The proposed graph structure was implemented in the graph database. Presentation of the history of geological stratum in relational databases is difficult. Studies show an example of stratum graph model, which enables data mining of stratum history in easy method, because graph database systems are designed to make search queries to find similarity in data. Additionally, the results of this study demonstrated useful query. Moreover, software and possible methods of construction of graph models were studied. As shown by the results, an analysis of complex models of geological stratum can be less complicated. Research shows that finding dependences in the graph representation of the geological layers can be beneficial in geological analyses.
CC-BY 4.0 License requiring that the original work has been properly cited.Obtaining, storing and sharing geological digital data in a form of images have been influenced by the dynamic development of information technology. Due to the fact that the multimedia data is not connected with a give PC or storage medium any more, but it is widely available as databases stored on network servers and accessed simultaneously by a number of users this suggests that data should be analyzed also in a new, for example parallel form. It necessitates defining the way in which the data in question is organized and interpreted efficiently.The aim of this discussion is to present the possibility to define image mining method for understanding and analyzing large image sets based on the methods of image analysis. The Authors present the concept of IT system developed to search for the images of particular rock structures in multimedia databases. Up till now, the use of databases in the image analysis process has been reduced to storing large amounts of data in form of images. However, all transformations and the analysis of such sets are performed on the user's PC. On the one hand, this requires a large data set (images) to be sent by network each time, but on the other hand this may also pose problems resulting from managing such large amounts of analyzed images on a PC. A solution for image exploration, which is based on any attributes extracted with computer image analysis with the use of extensions for server-side operations is proposed. The proposed approach eliminates these problems completely by moving all transformations of image analysis onto a database server platform. For this purpose, a set of routines realizing transformations of image analysis and mathematical morphology were developed. The proposed approach allows the unification of the image processing and analysis area as well as advanced statistical analysis of obtained parameters describing geometrical sized of the objects and images.The methodology is based exclusively on an image request, to which the system's response is a set of images presenting visually similar rocks (Ład-niak & Młynarczuk 2015). The search is based only on the query in the form of the image, while the system in response returns a collection of photographs representing the same structure of the rock. The proposed method is based on the methods of image analysis and the artificial intelligence, in particular, on the methods of data clustering and data classification. Decisions concerning the classification were based on six different methods of clustering and classification of data. As a result of the study showed that methods such as MEC, and based on the naive Bayes classifier and the method of the k-nearest neighbors have a very good classification results reaching for some of the studied rocks up to 100% correct decisions. To detect the similarity it is required to apply Determination of the characteristics identifying photographs (using image analysis algorithms), then verification of ...
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