A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Hausdorff-like metrics for fuzzy sets (poster session)
Pattern Recognition Letters
Note on Hausdorff-like metrics for fuzzy sets
Pattern Recognition Letters
A modified Hausdorff distance between fuzzy sets
Information Sciences: an International Journal
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Hausdorff distance measure for face recognition
Pattern Recognition
A new Hausdorff distance for image matching
Pattern Recognition Letters
The directed Hausdorff distance between imprecise point sets
Theoretical Computer Science
Gray Hausdorff distance measure for comparing face images
IEEE Transactions on Information Forensics and Security
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This paper proposes a new methodology for computing Hausdorff distances between sets of points in a robust way. In a first step, robust nearest neighbor distance distributions between the two sets of points are obtained by considering reliability measures in the computations through a Monte Carlo scheme. In a second step, the computed distributions are operated using random variables algebra in order to obtain probability distributions of the average, minimum or maximum distances. In the last step, different statistics are computed from these distributions. A statistical test of significance, the nearest neighbor index, in addition to the newly proposed divergence and clustering indices are used to compare the computed measurements with respect to values obtained by chance. Results on synthetic and real data show that the proposed method is more robust than the standard Hausdorff distance. In addition, unlike previously proposed methods based on thresholding, it is appropriate for problems that can be modeled through point processes.