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.
The Euclidean k-distance transformation in arbitrary dimensions: a separable implementation
2005-01-01
107419 byte
Conference paper
Electronic Resource
English
The Euclidean K-Distance Transformation in arbitrary Dimensions: A Separable Implementation
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