Unsupervised RGB-D image segmentation using joint clustering and region merging
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.17
Abstract
Recent advances in imaging sensors, such as Kinect, provide access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-axial clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner.
Session
3D and Stereo
Files
Extended Abstract (PDF, 1 page, 1.3M)Paper (PDF, 13 pages, 2.0M)
Bibtex File
Presentation
Citation
Md Abul Hasnat, Olivier Alata, and Alain Trémeau. Unsupervised RGB-D image segmentation using joint clustering and region merging. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.17
title = {Unsupervised RGB-D image segmentation using joint clustering and region merging},
author = {Hasnat, Md Abul and Alata, Olivier and Trémeau, Alain},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.17 }
}