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Unsupervised segmentation of unknown objects in complex environments

Asif, U., Bennamoun, M. and Sohel, F. (2015) Unsupervised segmentation of unknown objects in complex environments. Autonomous Robots, 40 (5). pp. 805-829.

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This paper presents a novel object segmentation approach for highly complex indoor scenes. Our approach starts with a novel algorithm which partitions the scene into distinct regions whose boundaries accurately conform to the physical object boundaries in the scene. Next, we propose a novel perceptual grouping algorithm based on local cues (e.g., 3D proximity, co-planarity, and shape convexity) to merge these regions into object hypotheses. Our extensive experimental evaluations demonstrate that our object segmentation results are superior compared to the state-of-the-art methods.

Item Type: Journal Article
Murdoch Affiliation: School of Veterinary and Biomedical Sciences
Publisher: Springer US
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