DocumentCode :
798434
Title :
Linear time Euclidean distance transform algorithms
Author :
Breu, Heinz ; Gil, Joseph ; Kirkpatrick, David ; Werman, Michael
Author_Institution :
Dept. of Comput. Sci., British Columbia Univ., Vancouver, BC, Canada
Volume :
17
Issue :
5
fYear :
1995
fDate :
5/1/1995 12:00:00 AM
Firstpage :
529
Lastpage :
533
Abstract :
Two linear time (and hence asymptotically optimal) algorithms for computing the Euclidean distance transform of a two-dimensional binary image are presented. The algorithms are based on the construction and regular sampling of the Voronoi diagram whose sites consist of the unit (feature) pixels in the image. The first algorithm, which is of primarily theoretical interest, constructs the complete Voronoi diagram. The second, more practical, algorithm constructs the Voronoi diagram where it intersects the horizontal lines passing through the image pixel centers. Extensions to higher dimensional images and to other distance functions are also discussed
Keywords :
computational geometry; image processing; transforms; Voronoi diagram; asymptotically optimal algorithms; linear time Euclidean distance transform algorithms; regular sampling; two-dimensional binary image; Computer vision; Councils; Euclidean distance; Histograms; Image databases; Indexing; Layout; Lighting; Object recognition; Optimized production technology;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.391389
Filename :
391389
Link To Document :
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