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Metric rectification for perspective images of planes

Liebowitz, D., Zisserman, A.·2002·Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)

Abstract

We describe the geometry constraints and algorithmic implementation for metric rectification of planes. The rectification allows metric properties, such as angles and length ratios, to be measured on the world plane from a perspective image. The novel contributions are: first, that in a stratified context the various forms of providing metric information, which include a known angle, two equal though unknown angles, and a known length ratio; can all be represented as circular constraints on the parameters of an affine transformation of the plane-this provides a simple and uniform framework for integrating constraints; second, direct rectification from right angles in the plane; third, it is shown that metric rectification enables calibration of the internal camera parameters; fourth, vanishing points are estimated using a Maximum Likelihood estimator; fifth, an algorithm for automatic rectification. Examples are given for a number of images, and applications demonstrated for texture map acquisition and metric measurements.

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Crossref · 10.1109/cvpr.1998.698649

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Metadata

Authors
Liebowitz, D., & Zisserman, A.
Venue
Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)
Published
2002-11-27
Pages
482-488
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2118002377
Cited by
340
References
12
FWCI
14.48
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metric rectification for perspective images of planes. https://app.cochat.ai/papers/10.1109/cvpr.1998.698649
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Liebowitz, D., & Zisserman, A. (2002). Metric rectification for perspective images of planes. Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231). https://doi.org/10.1109/cvpr.1998.698649

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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