The Euclidean k-distance transformation in arbitrary dimensions: A separable implementation

被引：0
|
作者：
Cuisenaire, O [1 ]
机构：
[1] Ecole Polytech Fed Lausanne, Signal Proc Inst ITS, CH-1015 Lausanne, Switzerland
来源：
2005 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), VOLS 1-5 | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 D1-dimensional problems and compare its accuracy and computational complexity to the existing raster-scanning and propagation approaches.
引用
收藏
页码：2409 / 2412
页数：4
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