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Université Jean Monnet – Saint-Etienne |  |
Journal articles
Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation
Abstract : Recent advances in depth imaging sensors provide easy 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-directional 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.
Cited literature [44 references]
https://hal-ujm.archives-ouvertes.fr/ujm-01273670
Contributor : Olivier Alata <>
Submitted on : Friday, February 12, 2016 - 5:32:26 PM
Last modification on : Monday, January 13, 2020 - 5:46:06 PM
Document(s) archivé(s) le : Saturday, November 12, 2016 - 7:58:59 PM
Contributor : Olivier Alata <>
Submitted on : Friday, February 12, 2016 - 5:32:26 PM
Last modification on : Monday, January 13, 2020 - 5:46:06 PM
Document(s) archivé(s) le : Saturday, November 12, 2016 - 7:58:59 PM
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pami_jcsd_rm_rev_draft_2.pdf
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