Towards automatic object segmentation with sequential multiple views

He, Hu, McKinnon, David N., & Upcroft, Ben (2011) Towards automatic object segmentation with sequential multiple views. In Drummond, Tom (Ed.) ACRA 2011 Proceedings, Australian Robotics & Automation Association, Monash University, Melbourne, VIC, pp. 1-7.

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Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments.

Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem.

Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.

Impact and interest:

3 citations in Scopus
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ID Code: 47889
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Segmentation, Multiple Views, Structure from Motion
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Current > QUT Faculties and Divisions > Creative Industries Faculty
Past > Institutes > Institute for Creative Industries and Innovation
Copyright Owner: Copyright 2011 Australian Robotics & Automation Association.
Deposited On: 22 Dec 2011 22:10
Last Modified: 22 Jun 2017 14:44

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