Document Detail


FOCUSR: feature oriented correspondence using spectral regularization--a method for precise surface matching.
MedLine Citation:
PMID:  23868776     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Existing methods for surface matching are limited by the tradeoff between precision and computational efficiency. Here, we present an improved algorithm for dense vertex-to-vertex correspondence that uses direct matching of features defined on a surface and improves it by using spectral correspondence as a regularization. This algorithm has the speed of both feature matching and spectral matching while exhibiting greatly improved precision (distance errors of 1.4 percent). The method, FOCUSR, incorporates implicitly such additional features to calculate the correspondence and relies on the smoothness of the lowest-frequency harmonics of a graph Laplacian to spatially regularize the features. In its simplest form, FOCUSR is an improved spectral correspondence method that nonrigidly deforms spectral embeddings. We provide here a full realization of spectral correspondence where virtually any feature can be used as an additional information using weights on graph edges, but also on graph nodes and as extra embedded coordinates. As an example, the full power of FOCUSR is demonstrated in a real-case scenario with the challenging task of brain surface matching across several individuals. Our results show that combining features and regularizing them in a spectral embedding greatly improves the matching precision (to a submillimeter level) while performing at much greater speed than existing methods.
Authors:
Herve Lombaert; Leo Grady; Jonathan R Polimeni; Farida Cheriet
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  35     ISSN:  1939-3539     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2013 Sep 
Date Detail:
Created Date:  2013-07-22     Completed Date:  2014-02-11     Revised Date:  2014-09-02    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2143-60     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Animals
Brain / anatomy & histology
Databases, Factual
Horses
Humans
Image Processing, Computer-Assisted / methods*
Models, Theoretical*
Software
Surface Properties
Grant Support
ID/Acronym/Agency:
K01 EB011498/EB/NIBIB NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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