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Superpixel Finite Element Segmentation for RGB-D Images

Kerr, Dermot, Coleman, Sonya and Bryan, Scotney (2017) Superpixel Finite Element Segmentation for RGB-D Images. In: 3rd International Conference on Robotics and Vision (ICRV 2017), Wuhan, China. IEEE. 5 pp. [Conference contribution]

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Abstract

Computer vision research has advanced from focusing solely on intensity images to the use of depth images, or combinations of RGB, intensity and depth images, mainly due to the recent development of low cost depth cameras. These images can be efficiently represented as a space-variant image by segmenting the images using a superpixel representation. Whilst superpixel representations offer advantages in terms of reduced processing requirements they present challenges in further processing as many existing image processing techniques require regularly distributed image data. We overcome this issue by making use of the Finite element framework for processing these images and demonstrate the application of the technique for detecting access holes in disaster management situations.

Item Type:Conference contribution (Paper)
Keywords:RGB-D imaging; image segmentation; SLIC; finite element framework; feature detection
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:38377
Deposited By: Dr Dermot Kerr
Deposited On:24 Jul 2017 09:14
Last Modified:24 Apr 2018 22:23

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