Superpixel Finite Element Segmentation for RGB-D Images

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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.
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages5
Publication statusAccepted/In press - 24 Apr 2017
Event3rd International Conference on Robotics and Vision (ICRV 2017) - Wuhan, China
Duration: 24 Apr 2017 → …


Conference3rd International Conference on Robotics and Vision (ICRV 2017)
Period24/04/17 → …
Internet address


  • RGB-D imaging
  • image segmentation
  • SLIC
  • finite element framework
  • feature detection


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