Superpixel Finite Element Segmentation for RGB-D Images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1-5
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 → …
http://www.icrv.org/

Conference

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

Fingerprint

Image processing
Processing
Disasters
Computer vision
Cameras
Costs

Keywords

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

Cite this

Kerr, D., Coleman, S., & Bryan, S. (Accepted/In press). Superpixel Finite Element Segmentation for RGB-D Images. In Unknown Host Publication (pp. 1-5)
@inproceedings{fe98ce497bd549cdb7570714ec7f96dd,
title = "Superpixel Finite Element Segmentation for RGB-D Images",
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.",
keywords = "RGB-D imaging, image segmentation, SLIC, finite element framework, feature detection",
author = "Dermot Kerr and Sonya Coleman and Scotney Bryan",
year = "2017",
month = "4",
day = "24",
language = "English",
pages = "1--5",
booktitle = "Unknown Host Publication",

}

Kerr, D, Coleman, S & Bryan, S 2017, Superpixel Finite Element Segmentation for RGB-D Images. in Unknown Host Publication. pp. 1-5, 3rd International Conference on Robotics and Vision (ICRV 2017), 24/04/17.

Superpixel Finite Element Segmentation for RGB-D Images. / Kerr, Dermot; Coleman, Sonya; Bryan, Scotney.

Unknown Host Publication. 2017. p. 1-5.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Superpixel Finite Element Segmentation for RGB-D Images

AU - Kerr, Dermot

AU - Coleman, Sonya

AU - Bryan, Scotney

PY - 2017/4/24

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N2 - 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.

AB - 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.

KW - RGB-D imaging

KW - image segmentation

KW - SLIC

KW - finite element framework

KW - feature detection

M3 - Conference contribution

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BT - Unknown Host Publication

ER -