Efficient computation of the minimum Hausdorff distance for visual recognition
Efficient computation of the minimum Hausdorff distance for visual recognition
Line-Based Recognition Using A Multidimensional Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Visual Recognition Using the Hausdorff Distance
Efficient Visual Recognition Using the Hausdorff Distance
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Line feature-based recognition using Hausdorff distance
ISCV '95 Proceedings of the International Symposium on Computer Vision
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
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Recently, a new image registration method, based on the Hausdorff fraction and a multi-resolution search of the transformation space, has been developed in the literature. This method has been applied to problems involving translations, translation and scale, and Affine transformations. In this paper, we adapt the above method to the set of similarity transformations. We also introduce a new variant of the Hausdorff fraction similarity measure based on a multi-class approach, which we call the Multi-class Hausdorff Fraction (MCHF). The multi-class approach is more efficient because it matches feature points only if they are from the same class. To validate our approach, we segment edge maps into two classes which are the class of straight lines and the class of curves, and we apply the new multi-class approach to two image registration examples, using synthetic and real images, respectively. Experimental results show that the multiclass approach speeds up the multi-resolution search algorithm.