GrabCutSFM : how 3D information improves unsupervised object segmentation

He, Hu & Upcroft, Ben (2013) GrabCutSFM : how 3D information improves unsupervised object segmentation. In Alici, Gursel & Moheimani, Reza (Eds.) Proceedings of the 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, Novotel Wollongong Northbeach, Wollongong, Australia, pp. 548-553.

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In this paper, we present an unsupervised graph cut based object segmentation method using 3D information provided by Structure from Motion (SFM), called Grab- CutSFM. Rather than focusing on the segmentation problem using a trained model or human intervention, our approach aims to achieve meaningful segmentation autonomously with direct application to vision based robotics. Generally, object (foreground) and background have certain discriminative geometric information in 3D space. By exploring the 3D information from multiple views, our proposed method can segment potential objects correctly and automatically compared to conventional unsupervised segmentation using only 2D visual cues. Experiments with real video data collected from indoor and outdoor environments verify the proposed approach.

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ID Code: 61429
Item Type: Conference Paper
Refereed: Yes
Keywords: image segmentation, unsupervised, markov random fields, grabcut, structure from motion
ISBN: 978-1-4673-5319-9
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2013 Please consult author(s)
Deposited On: 19 Jul 2013 01:01
Last Modified: 29 Aug 2013 23:07

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