An Efficient Algorithm for Calculating the Exact Hausdorff Distance

By Abdel Aziz Taha, Allan Hanbury. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

365 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7f9a1a8460201a5686672ce946f7631be6a41cf2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Klaus Hermann Maier-Hein
2. Fabian Isensee
3. David Flynn
4. Heinz-Peter Schlemmer
5. Michael G. Pecht
6. Valentin Robu
7. Septimiu E. Salcudean
8. Davood Karimi

In Signal Processing

1. Michael G. Pecht
2. Septimiu E. Salcudean
3. Davood Karimi

Citations in the corpus

• Machine learning pipeline for battery state-of-health estimation (2021, methodology): “[75] Abdel Aziz Taha and Allan Hanbury.”
• Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks (2019, methodology): “In this figure, we have plotted the HD estimated using Equation (7) versus the exact HD computed using [36] between the ground-truth and approximate segmentations”
• Automated brain extraction of multisequence MRI using artificial neural networks (2019, methodology): “Therefore, we used the Hausdorff distance (Taha & Hanbury, 2015) to measure the maxi- mal contour distance (mm) between the two masks. d x! yð Þ= max dx!yi , i=1::Nx Hausdorff distance GT,Mð Þ= max d GT!”