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2018 | OriginalPaper | Chapter

Object Proposal via Depth Connectivity Constrained Grouping

Authors : Yuantian Wang, Lei Huang, Tongwei Ren, Sheng-Hua Zhong, Yan Liu, Gangshan Wu

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Object proposal aims to detect category-independent object candidates with a limited number of bounding boxes. In this paper, we propose a novel object proposal method on RGB-D images with the constraint of depth connectivity, which can improve the key techniques in grouping based object proposal effectively, including segment generation, hypothesis expansion and candidate ranking. Given an RGB-D image, we first generate segments using depth aware hierarchical segmentation. Next, we combine the segments into hypotheses hierarchically on each level, and further expand these hypotheses to object candidates using depth connectivity constrained region growing. Finally, we score the object candidates based on their color and depth features, and select the ones with the highest scores as the object proposal result. We validated the proposed method on the largest RGB-D image data set for object proposal, and our method is superior to the state-of-the-art methods.

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Title: Object Proposal via Depth Connectivity Constrained Grouping
Authors: Yuantian Wang
Lei Huang
Tongwei Ren
Sheng-Hua Zhong
Yan Liu
Gangshan Wu
Publisher: Springer International Publishing
Book: Advances in Multimedia Information Processing – PCM 2017
Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-77383-4_4

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