Fast Exact Euclidean Distance (FEED) Transformation

Theo Schouten; J. Kittler; Egon van den Broek; M. Petrou; M. Nixon

doi:10.1109/ICPR.2004.1334599

Fast Exact Euclidean Distance (FEED) Transformation

Theo Schouten, J. Kittler (Editor), Egon van den Broek, M. Petrou (Editor), M. Nixon (Editor)

Research output: Contribution to conference › Paper › peer-review

20 Citations (Scopus)

107 Downloads (Pure)

Abstract

Fast Exact Euclidean Distance (FEED) transformation is introduced, starting from the inverse of the distance transformation. The prohibitive computational cost of a naive implementation of traditional Euclidean Distance Transformation, is tackled by three operations: restriction of both the number of object pixels and the number of background pixels taken in consideration and pre-computation of the Euclidean distance. Compared to the Shih and Liu 4-scan method the FEED algorithm is often faster and is less memory consuming.

Original language	Undefined
Pages	594-597
Number of pages	4
DOIs	https://doi.org/10.1109/ICPR.2004.1334599
Publication status	Published - 23 Aug 2004
Event	17th IEEE International Conference on Pattern Recognition 2004 - Cambridge, United Kingdom Duration: 23 Aug 2004 → 26 Aug 2004 Conference number: 17

Conference

Conference	17th IEEE International Conference on Pattern Recognition 2004
Abbreviated title	ICPR 2004
Country/Territory	United Kingdom
City	Cambridge
Period	23/08/04 → 26/08/04

Keywords

HMI-IE: Information Engineering
memory consumption
distance maps/transforms
Fast Exact Euclidean Distance (FEED)
IR-79286
Euclidean distance
Computational Complexity
EWI-21120

Access to Document

10.1109/ICPR.2004.1334599

Cite this

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title = "Fast Exact Euclidean Distance (FEED) Transformation",

abstract = "Fast Exact Euclidean Distance (FEED) transformation is introduced, starting from the inverse of the distance transformation. The prohibitive computational cost of a naive implementation of traditional Euclidean Distance Transformation, is tackled by three operations: restriction of both the number of object pixels and the number of background pixels taken in consideration and pre-computation of the Euclidean distance. Compared to the Shih and Liu 4-scan method the FEED algorithm is often faster and is less memory consuming.",

keywords = "HMI-IE: Information Engineering, memory consumption, distance maps/transforms, Fast Exact Euclidean Distance (FEED), IR-79286, Euclidean distance, Computational Complexity, EWI-21120",

author = "Theo Schouten and J. Kittler and {van den Broek}, Egon and M. Petrou and M. Nixon",

year = "2004",

month = aug,

day = "23",

doi = "10.1109/ICPR.2004.1334599",

language = "Undefined",

pages = "594--597",

note = "17th IEEE International Conference on Pattern Recognition 2004 ; Conference date: 23-08-2004 Through 26-08-2004",

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T1 - Fast Exact Euclidean Distance (FEED) Transformation

AU - Schouten, Theo

AU - van den Broek, Egon

A2 - Kittler, J.

A2 - Petrou, M.

A2 - Nixon, M.

N1 - Conference code: 17

PY - 2004/8/23

Y1 - 2004/8/23

N2 - Fast Exact Euclidean Distance (FEED) transformation is introduced, starting from the inverse of the distance transformation. The prohibitive computational cost of a naive implementation of traditional Euclidean Distance Transformation, is tackled by three operations: restriction of both the number of object pixels and the number of background pixels taken in consideration and pre-computation of the Euclidean distance. Compared to the Shih and Liu 4-scan method the FEED algorithm is often faster and is less memory consuming.

AB - Fast Exact Euclidean Distance (FEED) transformation is introduced, starting from the inverse of the distance transformation. The prohibitive computational cost of a naive implementation of traditional Euclidean Distance Transformation, is tackled by three operations: restriction of both the number of object pixels and the number of background pixels taken in consideration and pre-computation of the Euclidean distance. Compared to the Shih and Liu 4-scan method the FEED algorithm is often faster and is less memory consuming.

KW - HMI-IE: Information Engineering

KW - memory consumption

KW - distance maps/transforms

KW - Fast Exact Euclidean Distance (FEED)

KW - IR-79286

KW - Euclidean distance

KW - Computational Complexity

KW - EWI-21120

U2 - 10.1109/ICPR.2004.1334599

DO - 10.1109/ICPR.2004.1334599

M3 - Paper

SP - 594

EP - 597

T2 - 17th IEEE International Conference on Pattern Recognition 2004

Y2 - 23 August 2004 through 26 August 2004

ER -