Name
Abstract | ||
|---|---|---|
We propose a method for computing Euclidean distance transform (EDT) in 3D images.The method utilizes voxel coverage information to increase precision of EDT.The method can be used with any vector propagation based EDT in 3D.Synthetic tests confirm significant improvement in achieved precision.Both the related binary and the existing coverage based methods are outperformed. Distance transforms (DTs) are, usually, defined on a binary image as a mapping from each background element to the distance between its centre and the centre of the closest object element. However, due to discretization effects, such DTs have limited precision, including reduced rotational and translational invariance. We show in this paper that a significant improvement in performance of Euclidean DTs can be achieved if voxel coverage values are utilized and the position of an object boundary is estimated with sub-voxel precision. We propose two algorithms of linear time complexity for estimating Euclidean DT with sub-voxel precision. The evaluation confirms that both algorithms provide 4-14 times increased accuracy compared to what is achievable from a binary object representation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.patrec.2015.07.035 | Pattern Recognition Letters |
Keywords | Field | DocType |
Distance transform,Precision,Coverage representation,Vector propagation DT algorithm,Sub-voxel accuracy | Voxel,Discretization,Computer vision,Pattern recognition,Binary image,Euclidean distance,Distance transform,Artificial intelligence,Time complexity,Binary Object,Mathematics,Binary number | Journal |
Volume | Issue | ISSN |
65 | C | 0167-8655 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Ilic | 1 | 0 | 0.34 |
| Joakim Lindblad | 2 | 197 | 27.45 |
| Natasa Sladoje | 3 | 204 | 26.16 |
| IlićVladimir | 4 | 0 | 0.34 |