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

S Suganthan; SA Coleman; BW Scotney

doi:10.1007/978-3-540-79547-6_39

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Unknown Host Publication
Pages	403-412
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-540-79547-6_39
Publication status	Published (in print/issue) - May 2008
Event	6th International Conference on Computer Vision Systems, Vision for Cognitive Systems (ICVS 2008) - Santorini, Greece Duration: 1 May 2008 → …

Conference

Conference	6th International Conference on Computer Vision Systems, Vision for Cognitive Systems (ICVS 2008)
Period	1/05/08 → …

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10.1007/978-3-540-79547-6_39

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title = "Multiscale Laplacian Operators for Feature Extraction on Irregularly Distributed 3-D Range Data",

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.",

author = "S Suganthan and SA Coleman and BW Scotney",

year = "2008",

month = may,

doi = "10.1007/978-3-540-79547-6_39",

language = "English",

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note = "6th International Conference on Computer Vision Systems, Vision for Cognitive Systems (ICVS 2008) ; Conference date: 01-05-2008",

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T1 - Multiscale Laplacian Operators for Feature Extraction on Irregularly Distributed 3-D Range Data

AU - Suganthan, S

AU - Coleman, SA

AU - Scotney, BW

PY - 2008/5

Y1 - 2008/5

N2 - 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.

AB - 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.

UR - http://dx.doi.org/10.1007/978-3-540-79547-6

U2 - 10.1007/978-3-540-79547-6_39

DO - 10.1007/978-3-540-79547-6_39

M3 - Conference contribution

SP - 403

EP - 412

BT - Unknown Host Publication

T2 - 6th International Conference on Computer Vision Systems, Vision for Cognitive Systems (ICVS 2008)

Y2 - 1 May 2008

ER -

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

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