Graph-based RGB-D Image Segmentation Using Color-directional-region Merging
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen
Abstract
Color and depth information provided simultaneously in RGB-D images can be used to segment scenes into disjoint regions. In this paper, a graph-based segmentation method for RGB-D image is proposed, in which an adaptive data-driven combination of color- and normal-variation is presented to construct dissimilarity between two adjacent pixels and a novel region merging threshold exploiting normal information in adjacent regions is proposed to control the proceeding of the region merging. We evaluate our method on the NYU-v2 depth database and compare it with several published RGB-D partition methods. The experimental results show that our method is comparable with the state-of-the-art methods and provides more details of structures in the scene.
BibTeX
@inproceedings{icassp2019_graphbasedrgbdim,
title = {Graph-based RGB-D Image Segmentation Using Color-directional-region Merging},
author = {Xiong Pan and Zejun Zhang and Yizhang Liu and Changcai Yang and Qiufeng Chen and Li Cheng and Jiaxiang Lin and Riqing Chen},
booktitle = {ICASSP 2019},
year = {2019}
}