Robust comparison of binary images
Pattern Recognition Letters
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
Binary-image comparison with local-dissimilarity quantification
Pattern Recognition
A Non-symmetrical Method of Image Local-Difference Comparison for Ancient Impressions Dating
Graphics Recognition. Recent Advances and New Opportunities
Weak inclusions and digital spaces
Pattern Recognition Letters
Evaluation of Brain MRI Alignment with the Robust Hausdorff Distance Measures
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Parameter identification of 1D fractal interpolation functions using bounding volumes
Journal of Computational and Applied Mathematics
The Hausdorff fuzzy quasi-metric
Fuzzy Sets and Systems
Curve fitting by fractal interpolation
Transactions on computational science I
Classifying transformation-variant attributed point patterns
Pattern Recognition
Journal of Mathematical Imaging and Vision
Optimizing dissimilarity-based classifiers using a newly modified hausdorff distance
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
Hyperspaces of a weightable quasi-metric space: Application to models in the theory of computation
Mathematical and Computer Modelling: An International Journal
Hausdorff distance with k-nearest neighbors
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Image processing tools for better incorporation of 4D seismic data into reservoir models
Journal of Computational and Applied Mathematics
Class-dependent dissimilarity measures for multiple instance learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Robust estimation of distance between sets of points
Pattern Recognition Letters
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Object matching in two-dimensional images has been an important topic in computer vision, object recognition, and image analysis. The Hausdorff distance plays an important role in image matching. In order to deal with image matching problems in random noisy situations, a new Hausdorff distance is proposed in this paper. Unlike the other methods that match two binary images, the proposed method can match the gray images that have a few of pixel values. An example of object recognition is used to demonstrate the efficiency of the proposed method. The results show that, compared with MHD, the new Hausdorff distance can dispose of the noisy image matching in a more desirable manner, due to the fact that the comprehensive reflection of the gray information of neighbor pixels in the determination of the Hausdorff distance is taken into account. In addition, the proposed method can be implemented in an easy way.