2D Euclidean distance transform algorithms: A comparative survey
By Ricardo Fabbri, L. F. D. Costa, Julio C. Torelli, Odemir Martinez Bruno. CSUR, 2008
567 citations recorded by the source corpus.
The distance transform (DT) is a general operator forming the basis of many methods in computer vision and geometry, with great potential for practical applications. However, all the optimal algorithms for the computation of the exact Euclidean DT (EDT) were proposed only since the 1990s. In this work, state-of-the-art sequential 2D EDT algorithms are reviewed and compared, in an effort to reach more solid conclusions regarding their differences in speed and their exactness. Six of the best algorithms were fully implemented and compared in practice.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/3c3e09f71d4d0ec31ade6ce666fe2a1d0317e0ff
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Andrew Blake
- Antonio Criminisi
- Toby Sharp
- Brian L. Price
- Scott D. Cohen
- Jianfei Cai
- Angela Yao
- Soumajit Majumder
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrew Zisserman
- Jitendra Malik
- Xiaogang Wang
- Ming-Hsuan Yang
- Alan L. Yuille
- Li Fei-Fei
- Dacheng Tao
Citations in the corpus
- GeoS: Geodesic Image Segmentation (2008, background): “Excellent surveys of techniques for computing non-geodesic distance transforms may be found in [14,15].”