Unsupervised object individuation from RGB-D image sequences

Seongyong Koo, Dongheui Lee, Dong Soo Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In this paper, we propose a novel unified framework for unsupervised object individuation from RGB-D image sequences. The proposed framework integrates existing location-based and feature-based object segmentation methods to achieve both computational efficiency and robustness in unstructured and dynamic situations. Based on the infant's object indexing theory, the newly proposed ambiguity graph plays as a key component of the framework to detect falsely segmented objects and rectify them by using both location and feature information. In order to evaluate the proposed method, three table-top multiple object manipulation scenarios were performed: stacking, unstacking, and occluding tasks. The results showed that the proposed method is more robust than the location-only method and more efficient than the feature-only method.

Original languageEnglish
Title of host publicationIROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4450-4457
Number of pages8
ISBN (Electronic)9781479969340
DOIs
StatePublished - 31 Oct 2014
Externally publishedYes
Event2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States
Duration: 14 Sep 201418 Sep 2014

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Country/TerritoryUnited States
CityChicago
Period14/09/1418/09/14

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