Deviation magnification: Revealing departures from ideal geometries
Name
DeviationMagnification.pdf
Size
31.4 MB
Format
Adobe PDF
Checksum (MD5)
935bd9801f9c7b51a3df9079761c9c89
Author(s)
Wadhwa, Neal
Dekel, Tali
Wei, Donglai
Durand, Fredo
Freeman, William T.
Date Issued
November 2015
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Citation
Neal Wadhwa, Tali Dekel, Donglai Wei, Fredo Durand, and William T. Freeman. 2015. Deviation magnification: revealing departures from ideal geometries. ACM Trans. Graph. 34, 6, Article 226 (October 2015), 10 pages.
Version
Author's final manuscript
Abstract
Structures and objects are often supposed to have idealized geometries such as straight lines or circles. Although not always visible to the naked eye, in reality, these objects deviate from their idealized models. Our goal is to reveal and visualize such subtle geometric deviations, which can contain useful, surprising information about our world. Our framework, termed Deviation Magnification, takes a still image as input, fits parametric models to objects of interest, computes the geometric deviations, and renders an output image in which the departures from ideal geometries are exaggerated. We demonstrate the correctness and usefulness of our method through quantitative evaluation on a synthetic dataset and by application to challenging natural images.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2816795.2818109
