Automatic object segmentation on RGB-D data using surface normals and region similarity
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018, Funchal, Madeira, Portekiz, 27 - 29 Ocak 2018, cilt.4, ss.379-386, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 4
- Doi Numarası: 10.5220/0006617303790386
- Basıldığı Şehir: Funchal, Madeira
- Basıldığı Ülke: Portekiz
- Sayfa Sayıları: ss.379-386
- Anahtar Kelimeler: Object segmentation, Region growing, RGB-D data, Surface normals
- Hacettepe Üniversitesi Adresli: Evet
Özet
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.