Linear time Euclidean distance transform algorithms

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 t...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence 1995-05, Vol.17 (5), p.529-533
Main Authors:	Breu, H., Gil, J., Kirkpatrick, D., Werman, M.
Format:	Article
Language:	eng
Subjects:	Applied sciences Artificial intelligence Computer science control theory systems Computer vision Councils Euclidean distance Exact sciences and technology Histograms Image databases Indexing Layout Lighting Object recognition Optimized production technology Pattern recognition. Digital image processing. Computational geometry
Online Access:	Get full text
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Summary:	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.< >
ISSN:	0162-8828 1939-3539