@inproceedings{0f0e7ac725ed48e38a1359d9bb2a582d,
title = "Object proposal via depth connectivity constrained grouping",
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.",
keywords = "Constrained grouping, Depth connectivity, Object proposal, RGB-D image",
author = "Yuantian Wang and Lei Huang and Tongwei Ren and Zhong, {Sheng Hua} and Yan Liu and Gangshan Wu",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-77383-4_4",
language = "English",
isbn = "9783319773827",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "34--44",
editor = "Bing Zeng and Hongliang Li and Qingming Huang and {El Saddik}, Abdulmotaleb and Shuqiang Jiang and Xiaopeng Fan",
booktitle = "Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers",
note = "18th Pacific-Rim Conference on Multimedia, PCM 2017 ; Conference date: 28-09-2017 Through 29-09-2017",
}