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Scalable Operators for Feature Extraction on 3-D Data

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Suganthan, S, Coleman, SA and Scotney, BW (2008) Scalable Operators for Feature Extraction on 3-D Data. In: European Robotics Symposium 2008 (Euros 2008), Prague. UNSPECIFIED. 10 pp. [Conference contribution]

Full text not available from this repository.

URL: http://www.springerlink.com/content/yr4654x2t0005t46/

DOI: 10.1007/978-3-540-78317-6_27

Abstract

Real-time extraction of features from range images can play an important role in robotic vision tasks such as localisation and navigation. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Feature extraction on range data has proven to be a more complex problem than on intensity images due to both the irregular distribution of range images. This paper presents a general approach to the development of scalable derivative operators using a finite element framework that can be applied directly to processing regularly or irregularly distributed range image data. The gradient operators of varying scales are evaluated with respect to their performance on regular and irregular grids.

Item Type:Conference contribution (Paper)
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
Computer Science Research Institute > Information and Communication Engineering
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:6849
Deposited By:Professor Bryan Scotney
Deposited On:20 Jan 2010 15:40
Last Modified:15 Jun 2011 11:08

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