@inproceedings{65b3c64eed6a4400b7d229e3f84b1d39,
title = "Automatic object segmentation on RGB-D data using surface normals and region similarity",
abstract = "In this study, a method for automatic object segmentation on RGB-D data is proposed. Surface normals extracted from depth data are used to determine segment candidates first. Filtering steps are applied to depth map to get a better representation of the data. After filtering, an adapted version of region growing segmentation is performed using surface normal comparisons on depth data. Extracted surface segments are then compared with their spatial color similarity and depth proximity, and finally region merging is applied to obtain object segments. The method is tested on a well-known dataset, which has some complex table-top scenes containing multiple objects. The method produces comparable segmentation results according to related works.",
keywords = "Object segmentation, RGB-D data, Region growing, Surface normals",
author = "Yalic, \{Hamdi Yalin\} and Can, \{Ahmet Burak\}",
note = "Publisher Copyright: Copyright {\textcopyright} 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018 ; Conference date: 27-01-2018 Through 29-01-2018",
year = "2018",
doi = "10.5220/0006617303790386",
language = "English",
series = "VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",
publisher = "SciTePress",
pages = "379--386",
editor = "Alain Tremeau and Francisco Imai and Jose Braz",
booktitle = "VISAPP",
}