Graph-based RGB-D Image Segmentation Using Color-directional-region Merging

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...

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Bibliographic Details
Main Authors: Pan, Xiong, Zhang, Zejun, Liu, Yizhang, Yang, Changcai, Chen, Qiufeng, Cheng, Li, Lin, Jiaxiang, Chen, Riqing
Format: Conference Proceeding
Language:English
Subjects:
Agriculture
Big Data
color-normal-region merging
data-driven weight
Forestry
graph-based segmentation
Image color analysis
Image segmentation
Merging
RGB-D image segmentation
Three-dimensional displays
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Description
Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8682701