The Euclidean Distance Transform Applied to the FCC and BCC Grids
The discrete Euclidean distance transform is applied to grids with non-cubic voxels, the face-centered cubic (fcc) and body-centered cubic (bcc) grids. These grids are three-dimensional generalizations of the hexagonal grid. Raster scanning and contour processing techniques are applied using different neighbourhoods. When computing the Euclidean distance transform, some voxel configurations produce errors. The maximum errors for the two different grids and neighbourhood sizes are analyzed and compared with the cubic grid.
KeywordsEuclidean Distance Grid Point Maximum Error Voronoi Diagram Maximum Relative Error
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- 5.Ragnemalm, I.: The Euclidean distance transform and its implementation on SIMD architectures. In: Proceedings of 6th Scandinavian Conference on Image Analysis, Oulu, Finland, pp. 379–384 (1989)Google Scholar
- 7.Strand, R., Borgefors, G.: Distance transforms for three-dimensional grids with non-cubic voxels (2004) (Submitted for publication)Google Scholar
- 8.Vincent, L.: Exact Euclidean distance function by chain propagations. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, pp. 520–525 (1991)Google Scholar
- 11.Yamada, H.: Complete Euclidean distance transformation by parallel operation. In: Proceedings 7th international Conference on Pattern Recognition, Montreal, pp. 69–71 (1984)Google Scholar