Minimum description length with local geometry

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Publication Type pre-print
School or College <blank>
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Creator Gerig, Guido
Other Author Styner, Martin; Oguz, Ipek; Heimann, Tobias
Title Minimum description length with local geometry
Date 2008-01-01
Description Establishing optimal correspondence across object populations is essential to statistical shape analysis. Minimizing the description length (MDL) is a popular method for finding correspondence. In this work, we extend the MDL method by incorporating various local curvature metrics. Using local curvature can improve performance by ensuring that corresponding points exhibit similar local geometric characteristics that can't always be captured by mere point locations. We illustrate results on a variety of anatomical structures. The MDL method with a combination of point locations and curvature outperforms all the other methods we analyzed, including traditional MDL and spherical harmonics (SPHARM) correspondence, when the analyzed object population exhibits complex structure. When the objects are of simple nature, however, there's no added benefit to using the local curvature. In our experiments, we did not observe a significant difference in the correspondence quality when different curvature metrics (e.g. principal curvatures, mean curvature, Gaussian curvature) were used.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 1283
Last Page 1286
Language eng
Bibliographic Citation Styner, M., Oguz, I., Heimann, T., & Gerig, G. (2008). Minimum description length with local geometry. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1283-6.
Rights Management (c) 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 220,645 bytes
Identifier uspace,19239
ARK ark:/87278/s61z7dj8
Setname ir_uspace
Date Created 2015-01-30
Date Modified 2021-05-06
ID 712796
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