research-article

Comparing Images Using the Hausdorff Distance

Authors:

D. P. Huttenlocher,

G. A. Klanderman,

W. A. RucklidgeAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 15, Issue 9

Pages 850 - 863

https://doi.org/10.1109/34.232073

Published: 01 September 1993 Publication History

Abstract

The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image.

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Comparing Images Using the Hausdorff Distance
1. Computing methodologies
  1. Computer graphics
  2. Machine learning
    1. Machine learning algorithms

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 15, Issue 9

September 1993

125 pages

ISSN:0162-8828

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Copyright © Copyright © 1993 IEEE. All Rights Reserved.

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IEEE Computer Society

United States

Publication History

Published: 01 September 1993

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Cited By

Yang YDastani MSichman JAlechina NDignum V(2024)On the Complexity of Candidates-Embedded Multiwinner Voting under the Hausdorff FunctionProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663229(2567-2569)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663229
Maquiling VByrne SNiehorster DNyström MKasneci E(2024)Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)Proceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36547047:2(1-16)Online publication date: 17-May-2024
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Liu DHu WLi X(2024)Robust Geometry-Dependent Attack for 3D Point CloudsIEEE Transactions on Multimedia10.1109/TMM.2023.330489626(2866-2877)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3304896
Bian YTian SLiu X(2024)iBA: Backdoor Attack on 3D Point Cloud via Reconstructing ItselfIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.345263019(7994-8008)Online publication date: 1-Jan-2024
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