Journal article
Unsupervised segmentation of unknown objects in complex environments
Autonomous Robots, Vol.40(5), pp.805-829
2015
Abstract
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.
Details
- Title
- Unsupervised segmentation of unknown objects in complex environments
- Authors/Creators
- U. Asif (Author/Creator) - The University of Western AustraliaM. Bennamoun (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - Murdoch University
- Publication Details
- Autonomous Robots, Vol.40(5), pp.805-829
- Publisher
- Springer US
- Identifiers
- 991005545159507891
- Murdoch Affiliation
- School of Veterinary and Biomedical Sciences
- Language
- English
- Resource Type
- Journal article
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Source: InCites
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.116 Robotics
- 4.116.133 Simultaneous Localization and Mapping
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Robotics
- ESI research areas
- Engineering