The Euclidean k-Distance Transformartion in Arbitrary Dimensions - a Separable Implementation

The signed k-distance transformation (k-DT) computes the k nearest prototypes from each location on a discrete regular grid within a given D dimensional volume. We propose a new k-DT algorithm that divides the problem into D 1-dimensional problems and compare its accuracy and computational complexity to the existing raster-scanning and propagation approaches.


Publié dans:
International Conference on Image Processing - ICIP'05, 2, 658-661
Année
2005
Publisher:
EUSIPCO
Mots-clefs:
Laboratoires:




 Notice créée le 2006-06-14, modifiée le 2019-12-05

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