Neighborhoods for distance transformations using ordered propagation
CVGIP: Image Understanding
A fast level set method for propagating interfaces
Journal of Computational Physics
An $\cal O(N)$ Level Set Method for Eikonal Equations
SIAM Journal on Scientific Computing
Efficiently determining a locally exact shortest path on polyhedral surfaces
Computer-Aided Design
Finding the minimum-cost path without cutting corners
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Fully isotropic fast marching methods on Cartesian grids
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Fully isotropic fast marching methods on cartesian grids
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
An application of circumscribed circle filter in the Multi-Stencils Fast Marching method
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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In most, if not all fast marching methods published hitherto, the input cost function and the output arrival time are sampled on exactly the same grid. But since the input data samples are differences of the output samples we found it natural to separate the input and output grid half a sampling unit in all coordinates (two or three).We also employ 8-neighborhood (26-neighborhood in the 3D-case) in the basic updating step of the algorithm. Some simple numerical experiments verify that the modified method improves the accuracy considerably. However, we also feel the modified method leads itself more naturally to image processing applications like tracking and segmentation.