ML Anthology

Extraction of Maximal Inscribed Disks from Discrete Euclidean Distance Maps

Yaorong Ge, J. Michael Fitzpatrick
CVPR 1996 pp. 556-561
doi:10.1109/CVPR.1996.517127 /cvpr/1996/ge1996cvpr-extraction/

Abstract

Detection of the set of centers of maximal disks is a key step toward generation of accurate skeletons on the basis of distance maps. Algorithms using approximate distance metrics are abundant and their theory has been well established. However, the resulting skeletons may be inaccurate and sensitive to rotation. In this paper, we study methods for detecting maximal disks from distance maps based on the exact Euclidean metric. We first show that no previous algorithm identifies the exact set of discrete maximal disks under Euclidean distance metric. We then propose new algorithms and show that they produce the exact set of maximal disks. The effectiveness of our algorithms is demonstrated with numerous examples.

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Cite

Text

Ge and Fitzpatrick. "Extraction of Maximal Inscribed Disks from Discrete Euclidean Distance Maps." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996. doi:10.1109/CVPR.1996.517127

Markdown

[Ge and Fitzpatrick. "Extraction of Maximal Inscribed Disks from Discrete Euclidean Distance Maps." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996.](https://mlanthology.org/cvpr/1996/ge1996cvpr-extraction/) doi:10.1109/CVPR.1996.517127

BibTeX

@inproceedings{ge1996cvpr-extraction,
  title     = {{Extraction of Maximal Inscribed Disks from Discrete Euclidean Distance Maps}},
  author    = {Ge, Yaorong and Fitzpatrick, J. Michael},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1996},
  pages     = {556-561},
  doi       = {10.1109/CVPR.1996.517127},
  url       = {https://mlanthology.org/cvpr/1996/ge1996cvpr-extraction/}
}