Weighted distance mapping (WDM)

Egon van den Broek; N. Canagarajah; A. Chalmers; Theo E. Schouten; Peter M.F. Kisters; F. Deravi; S. Gibson; Harco C. Kuppens; P. Hobson; M. Mirmehdi; S. Marshall

doi:10.1049/cp:20050085

Weighted distance mapping (WDM)

Egon van den Broek, N. Canagarajah (Editor), A. Chalmers (Editor), Theo E. Schouten, Peter M.F. Kisters, F. Deravi (Editor), S. Gibson (Editor), Harco C. Kuppens, P. Hobson (Editor), M. Mirmehdi (Editor), S. Marshall (Editor)

Research output: Contribution to conference › Paper › Academic

6 Citations (Scopus)

21 Downloads (Pure)

Abstract

A new distance mapping technique is introduced: weighted distance mapping (WDM). It is based on an adapted version of Fast Exact Euclidean Distance (FEED) transforms. It computes, after assigning a metric, a probability space for partly categorized or clustered data. This is visualized by gradual intensity changes as illustrated by the categorization of a color space based on clustered data points. In addition, edge detection of boundaries between categories can be done to find exact borders of clusters or categories. Hence, Voronoi diagrams can be created. The proposed WDMs, with or without exact edges, provide a new rich source for data analysis as well as an intuitive method of describing structure in data.

Original language	Undefined
Pages	157-164
Number of pages	8
DOIs	https://doi.org/10.1049/cp:20050085
Publication status	Published - 4 Apr 2005
Event	IEE International Conference on Visual Information Engineering, VIE 2005 - Glasgow, Scotland, UK Duration: 4 Apr 2005 → 6 Apr 2005

Conference

Conference	IEE International Conference on Visual Information Engineering, VIE 2005
Period	4/04/05 → 6/04/05
Other	4-6 April 2005

Keywords

HMI-VRG: Virtual Reality and Graphics
fast exact Euclidean distance
data clusters
data inspection
data point distance
data space description
data space visualization
EWI-21123
probability space
IR-79125
Data Analysis
FEED transform
Edge detection
Voronoi diagrams
Weighted distance mapping

Access to Document

10.1049/cp:20050085

vandenBroek05-Weighted_Distance_Mapping_WDM
open

Cite this

van den Broek, E., Canagarajah, N. (Ed.), Chalmers, A. (Ed.), Schouten, T. E., Kisters, P. M. F., Deravi, F. (Ed.), Gibson, S. (Ed.), Kuppens, H. C., Hobson, P. (Ed.), Mirmehdi, M. (Ed.), & Marshall, S. (Ed.) (2005). Weighted distance mapping (WDM). 157-164. Paper presented at IEE International Conference on Visual Information Engineering, VIE 2005, . https://doi.org/10.1049/cp:20050085

van den Broek, Egon ; Canagarajah, N. (Editor) ; Chalmers, A. (Editor) ; Schouten, Theo E. ; Kisters, Peter M.F. ; Deravi, F. (Editor) ; Gibson, S. (Editor) ; Kuppens, Harco C. ; Hobson, P. (Editor) ; Mirmehdi, M. (Editor) ; Marshall, S. (Editor). / Weighted distance mapping (WDM). Paper presented at IEE International Conference on Visual Information Engineering, VIE 2005, .8 p.

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abstract = "A new distance mapping technique is introduced: weighted distance mapping (WDM). It is based on an adapted version of Fast Exact Euclidean Distance (FEED) transforms. It computes, after assigning a metric, a probability space for partly categorized or clustered data. This is visualized by gradual intensity changes as illustrated by the categorization of a color space based on clustered data points. In addition, edge detection of boundaries between categories can be done to find exact borders of clusters or categories. Hence, Voronoi diagrams can be created. The proposed WDMs, with or without exact edges, provide a new rich source for data analysis as well as an intuitive method of describing structure in data.",

keywords = "HMI-VRG: Virtual Reality and Graphics, fast exact Euclidean distance, data clusters, data inspection, data point distance, data space description, data space visualization, EWI-21123, probability space, IR-79125, Data Analysis, FEED transform, Edge detection, Voronoi diagrams, Weighted distance mapping",

author = "{van den Broek}, Egon and N. Canagarajah and A. Chalmers and Schouten, {Theo E.} and Kisters, {Peter M.F.} and F. Deravi and S. Gibson and Kuppens, {Harco C.} and P. Hobson and M. Mirmehdi and S. Marshall",

year = "2005",

month = apr,

day = "4",

doi = "10.1049/cp:20050085",

language = "Undefined",

pages = "157--164",

note = "null ; Conference date: 04-04-2005 Through 06-04-2005",

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van den Broek, E, Canagarajah, N (ed.), Chalmers, A (ed.), Schouten, TE, Kisters, PMF, Deravi, F (ed.), Gibson, S (ed.), Kuppens, HC, Hobson, P (ed.), Mirmehdi, M (ed.) & Marshall, S (ed.) 2005, 'Weighted distance mapping (WDM)', Paper presented at IEE International Conference on Visual Information Engineering, VIE 2005, 4/04/05 - 6/04/05 pp. 157-164. https://doi.org/10.1049/cp:20050085

Weighted distance mapping (WDM). / van den Broek, Egon; Canagarajah, N. (Editor); Chalmers, A. (Editor); Schouten, Theo E.; Kisters, Peter M.F.; Deravi, F. (Editor); Gibson, S. (Editor); Kuppens, Harco C.; Hobson, P. (Editor); Mirmehdi, M. (Editor); Marshall, S. (Editor).

2005. 157-164 Paper presented at IEE International Conference on Visual Information Engineering, VIE 2005, .

Research output: Contribution to conference › Paper › Academic

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AU - van den Broek, Egon

AU - Schouten, Theo E.

AU - Kisters, Peter M.F.

AU - Kuppens, Harco C.

A2 - Canagarajah, N.

A2 - Chalmers, A.

A2 - Deravi, F.

A2 - Gibson, S.

A2 - Hobson, P.

A2 - Mirmehdi, M.

A2 - Marshall, S.

PY - 2005/4/4

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N2 - A new distance mapping technique is introduced: weighted distance mapping (WDM). It is based on an adapted version of Fast Exact Euclidean Distance (FEED) transforms. It computes, after assigning a metric, a probability space for partly categorized or clustered data. This is visualized by gradual intensity changes as illustrated by the categorization of a color space based on clustered data points. In addition, edge detection of boundaries between categories can be done to find exact borders of clusters or categories. Hence, Voronoi diagrams can be created. The proposed WDMs, with or without exact edges, provide a new rich source for data analysis as well as an intuitive method of describing structure in data.

AB - A new distance mapping technique is introduced: weighted distance mapping (WDM). It is based on an adapted version of Fast Exact Euclidean Distance (FEED) transforms. It computes, after assigning a metric, a probability space for partly categorized or clustered data. This is visualized by gradual intensity changes as illustrated by the categorization of a color space based on clustered data points. In addition, edge detection of boundaries between categories can be done to find exact borders of clusters or categories. Hence, Voronoi diagrams can be created. The proposed WDMs, with or without exact edges, provide a new rich source for data analysis as well as an intuitive method of describing structure in data.

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KW - fast exact Euclidean distance

KW - data clusters

KW - data inspection

KW - data point distance

KW - data space description

KW - data space visualization

KW - EWI-21123

KW - probability space

KW - IR-79125

KW - Data Analysis

KW - FEED transform

KW - Edge detection

KW - Voronoi diagrams

KW - Weighted distance mapping

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DO - 10.1049/cp:20050085

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Y2 - 4 April 2005 through 6 April 2005

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