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EN
In this article, a method for insulator image classification is shown. For this purpose a Bag of Words algorithm, which was implemented in the Matlab environment, was used. In addition the Support Vector Machine algo-rithm was used for classification purpose and the Speeded Up Robust Features algorithm was employ for features description. Both algorithms are also parts of the Matlab environment. This paper includes also a description of BoW (Bag of Words) creation process. Some of the method limitation have also been present-ed.
EN
In this article, a method for insulator detection is shown. For this purpose a SURF algorithm was used. Overhead line construction, characteristics and functions have also been presented. This paper also includes a comparison of SURF and SIFT algorithms. Key SURF features from reference insulator image ware match to scene features to locate the insulator. Moreover the MSAC method was employ for estimation of geometric transformation.
EN
The purpose of this work is to outline the problems with insulators detection with computer vision and image processing methods. A basic power line insulator detection algorithm is presented. The algorithm used a HSV (Hue, Saturation, Intensity) color model, color thresholding and morphological erosion for image filtering. An important element of the work is the analysis of the reliability of the overhead line insulators and the impact of their failure on the line maintenance.
EN
In this article, a method for receiving point clouds from Microsoft Kinect sensor is shown. Microsoft Kinect construction, capabilities, functions and some basic point cloud processing method have also been presented. The depth information from the MS Kinect was converted to a point cloud using Point Cloud Library. For point cloud filtering the voxelized grid approach was apply. To detect pipes clusters author use Euclidean cluster extraction. Moreover the Ransac method was employed for plane segmentation.
PL
W artykule opisano metodę odbierania chmury punktów z czujnika Microsoft Kinect. Przedstawiono konstrukcję czujnika Microsoft Kinect, jego możliwości oraz wybrane podstawowe metody przetwarzania chmury punktów. Informacje otrzymane z czujnika MS Kinect zostały przekształcone w chmurze punktów przy wykorzystaniu Point Cloud Library.
EN
This paper presents a method which allows to determine characteristics of studio microphones and find for them the best possible studio application, using created research environment.
PL
W artykule przedstawiono metodę, która pozwala na określenie charakterystyk mikrofonów studyjnych przy wykorzystaniu stworzonego środowiska badawczego. Ponadto artykuł zawiera opis różnych typów mikrofonów i metodyki pozwalającej na znalezienie dla nich możliwie najlepszych zastosowań studyjnych. W trakcie studiów przebadano również dla badanych mikrofonów takie parametry jak kierunkowość, charakterystyki częstotliwościowe, odpowiedzi częstotliwościowej i wrażliwości. W artykule opisano także praktyczną przydatność wybranych metod.
EN
This paper presents a method which allows to determine characteristics of studio microphones, using created research environment. Moreover the article contains a description of different types of microphones and a method which allows to find for them the best possible studio application. During study directivity, frequency characteristics, frequency response and sensitivity of the selected microphones were also examined. In addition this article describes the usefulness of selected methods and implemented elements.
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