Automatic object segmentation of unstructured scenes using colour and depth maps

He, Hu & Upcroft, Ben (2013) Automatic object segmentation of unstructured scenes using colour and depth maps. IET Computer Vision.

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This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.

Impact and interest:

3 citations in Scopus
1 citations in Web of Science®
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211 since deposited on 19 Jul 2013
18 in the past twelve months

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ID Code: 61428
Item Type: Journal Article
Refereed: Yes
Keywords: image segmentation, structure from motion, depth estimation, graphcut, markov random fields
DOI: 10.1049/iet-cvi.2013.0018
ISSN: 1751-9640
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
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 The Institute of Engineering and Technology
Copyright Statement: This paper is a preprint of a paper accepted by IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at IET Digital Library.
Deposited On: 19 Jul 2013 01:11
Last Modified: 31 Oct 2016 16:39

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