Comparing Images Using the Hausdorff Distance

By Dan Huttenlocher, Gregory A. Klanderman, William Rucklidge. 1993

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Citations in the corpus

Topology matching for fully automatic similarity estimation of 3D shapes (2001, methodology): “When dealing with 2D images, techniques have been proposed for recognizing a silhouette or contour curve using properties of shape, such as curvature [10, 15, 19, 24, 27, 34] or using properties of image, such as color, texture or wavelet coefficient [12, 17, 20].”
Metrics Reloaded: Recommendations for image analysis validation (2022, unknown): “Xth Percentile Hausdorff Distance Xth Percentile HD [42] [0,∞) ↓ x x”
A new point matching algorithm for non-rigid registration (2003, methodology): “, tree searches [2,20], the Hausdorff distance [25], geometric hashing [24,26] and the alignment method [42].”
A framework for evaluating image segmentation algorithms (2006, background): “Many of the researchers who do evaluate their segmentation algorithms do so only on a limited number of components, such as cost analysis [10], inter-rater reliability [11], overall volume [12], or the Hausdorff distance [13].”
Class Representation and Image Retrieval with Non-Metric Distances (1998, methodology): “As one example, Huttenlocher et al.[17, 18] perform recognition and motion tracking by comparing point sets using the Hausdorff distance.”
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation (2018, methodology): “Traditional methods mainly rely on template matching techniques [16, 12, 14, 42], which are sensitive to cluttered environments and appearance changes.”
A robust algorithm for point set registration using mixture of Gaussians (2005, methodology): “Another elegant method is the partial Hausdorff distance registration [3] which incorporates an underlying robust mechanism similar to the least median of”
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI (2016, background): “• The Hausdorff distance is a symmetric measure of distance between two contours (Huttenlocher et al., 1993), and is defined as: where d ( · , · ) denotes Euclidean distance.”
Population-Averaged Atlas of the Macroscale Human Structural Connectome and Its Network Topology (2018, unknown): “We measured the Hausdorff distance (Huttenlocher et al., 1993; Pujol et al., 2015) between a pair of streamlines X and Y as”
Distance based similarity models for content based multimedia retrieval (2013, background): “Originally defined as a distance between sets, it has been modified and applied to a wide variety of problems in computer science, for instance for image comparison [Huttenlocher et al., 1993], face recognition [Chen and Lovell, 2010, Vivek and Sudha, 2007] and object location [Rucklidge, 1997].”
Data clustering: a review (1999, methodology): “Recently, several researchers [Huttenlocher et al. 1993; Dubuisson and Jain 1994] have used the Hausdorff distance in a point set matching context.”
Real-Time Seamless Single Shot 6D Object Pose Prediction (2017, methodology): “Other related methods include 3D model-based registration [17, 20, 35], Hausdorff matching [9], oriented Chamfer matching for edges [18] and 3D chamfer matching for aligning 3D curve-based models to images [26].”