20.500.12556/RUL-135600 Depth-image segmentation based on evolving principles for 3D sensing of structured indoor environments This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner. depth sensor line extraction flat surface extraction evolving clustering machine vision smart sensors globinski senzorji ekstrakcija daljic ekstrakcija ravnih površin samorazvijajoče se rojenje strojni vid pametni senzorji true false true Angleški jezik Slovenski jezik Članek v reviji 2022-03-22 08:43:18 2022-03-22 08:43:20 2022-09-14 04:14:11 0000-00-00 00:00:00 2021 0 0 30 str. iss. 13, art. 4395 Vol. 21 Jul. 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 681.5:004 1424-8220 10.3390/s21134395 74209795 RAZ_Antic_Milos_2021.pdf RAZ_Antic_Milos_2021.pdf 1 B205BD67EACBA8CB6ABCA6D832730CDC adf15396263c83a21ce33339a6f0cfde2fd431c59e1fabf9bf6309019e4e2922 f8708274-a9b3-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=154767 https://www.mdpi.com/1424-8220/21/13/4395 1 bf384527-a9b3-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=154766 Fakulteta za elektrotehniko 0 0 0