Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture recognition using the Perseus architecture
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Learning and Evaluating Visual Features for Pose Estimation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Hausdorff Kernel for 3D Object Acquisition and Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Kernel-Based 3D Object Representation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
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In this paper we propose a method for measuring the similarity between two images inspired by the notion of Hausdorff distance. Given two images, the method checks pixelwise if the grey values of one are contained in an appropriate interval around the corresponding grey values of the other. Under certain assumptions, this provides a tight bound on the directed Hausdorff distance of the two grey-level surfaces. The proposed technique can be seen as an equivalent in the grey level case of a matching method developed for the binary case by Huttenlocher et al. [2]. The method fits naturally an implementation based on comparison of data structures and requires no numerical computations whatsoever. Moreover, it is able to match images successfully in the presence of severe occlusions. The range of possible applications is vast; we present preliminary, very good results on stereo and motion correspondence and iconic indexing in real images, with and without occlusion.