Multiscale Laplacian Operators for Feature Extraction on Irregularly Distributed 3-D Range Data

S Suganthan, SA Coleman, BW Scotney

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

Abstract

Multiscale feature extraction in image data has been investigated for many years. More recently the problem of processing images containing irregularly distribution data has became prominent. We present a multiscale Laplacian approach that can be applied directly to irregularly distributed data and in particular we focus on irregularly distributed 3D range data. Our results illustrate that the approach works well over a range of irregular distributed and that the use of Laplacian operators on range data is much less susceptive to noise than the equivalent operators used on intensity data.
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages403-412
Number of pages10
DOIs
Publication statusPublished - May 2008
Event6th International Conference on Computer Vision Systems, Vision for Cognitive Systems (ICVS 2008) - Santorini, Greece
Duration: 1 May 2008 → …

Conference

Conference6th International Conference on Computer Vision Systems, Vision for Cognitive Systems (ICVS 2008)
Period1/05/08 → …

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