The paper aims at presentation of results of research on integration of image and laser data based on selected example. Since a few years the authors have been conducting research on processing image data, and those obtained from laser scanning in the form of the so-called point cloud. In experiments data from terrestrial and mobile laser scanning gained for two different objects were compared: a parish house from Goźlice located in the open-air ethnographic museum at the village of Tokarnia, Poland, and part of the Cracow-Warsaw railway line. The results of those experiments proved that data in the form of point cloud were not always sufficient for a precise 3D model reconstruction. Supplementing point clouds with photogrammetric images seems to be the best solution.
The paper aims at presentation of results of research on detection and recognition of selected class railway signs (W11p). When conducting the research, the authors have proposed their own algorithm, which achieved about 90% effectiveness at detecting W11p signs and 98% effectiveness at classifying them. The processes of localisation, segmentation and recognition of W11p signs were considerably simplified thanks to the application of backpropagation neural network. The authors believe that two non-standard methods related to the use of the network deserve attention: the application of an interactive method of generating the training set, owing to which also pixels highly diversified in terms of their colours could be included, and the use of a full spectrum of neural network responses, which made it possible to accomplish a feedback. It consisted in an automatic adjusting of the network responses’ threshold to the results of segmentation and recognition.
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