Matches in SemOpenAlex for { <https://semopenalex.org/work/W2085997191> ?p ?o ?g. }
- W2085997191 endingPage "333" @default.
- W2085997191 startingPage "317" @default.
- W2085997191 abstract "Motivated by the problem of nonparametric inference in high level digital image analysis, we introduce a general extrinsic approach for data analysis on Hilbert manifolds with a focus on means of probability distributions on such sample spaces. To perform inference on these means, we appeal to the concept of neighborhood hypotheses from functional data analysis and derive a one-sample test. We then consider the analysis of shapes of contours lying in the plane. By embedding the corresponding sample space of such shapes, which is a Hilbert manifold, into a space of Hilbert–Schmidt operators, we can define extrinsic mean shapes of random planar contours and their sample analogues. We then apply the general methods to this problem while considering the computational restrictions faced when utilizing digital imaging data. Comparisons of computational cost are provided to another method for analyzing shapes of contours." @default.
- W2085997191 created "2016-06-24" @default.
- W2085997191 creator A5062722872 @default.
- W2085997191 creator A5063510610 @default.
- W2085997191 creator A5075389310 @default.
- W2085997191 date "2013-11-01" @default.
- W2085997191 modified "2023-10-11" @default.
- W2085997191 title "Nonparametric estimation of means on Hilbert manifolds and extrinsic analysis of mean shapes of contours" @default.
- W2085997191 cites W187257440 @default.
- W2085997191 cites W1973654654 @default.
- W2085997191 cites W1983960282 @default.
- W2085997191 cites W2002859461 @default.
- W2085997191 cites W2011931375 @default.
- W2085997191 cites W2014770458 @default.
- W2085997191 cites W2025778152 @default.
- W2085997191 cites W2026231217 @default.
- W2085997191 cites W2036705201 @default.
- W2085997191 cites W2037095848 @default.
- W2085997191 cites W2049998781 @default.
- W2085997191 cites W2063023966 @default.
- W2085997191 cites W2072931016 @default.
- W2085997191 cites W2075187577 @default.
- W2085997191 cites W2087219114 @default.
- W2085997191 cites W2087358446 @default.
- W2085997191 cites W2091804476 @default.
- W2085997191 cites W2093976044 @default.
- W2085997191 cites W2095444191 @default.
- W2085997191 cites W2117897510 @default.
- W2085997191 cites W2122827492 @default.
- W2085997191 cites W2131529365 @default.
- W2085997191 cites W2139656229 @default.
- W2085997191 cites W2140798815 @default.
- W2085997191 cites W2146932984 @default.
- W2085997191 cites W2166301483 @default.
- W2085997191 cites W2951656971 @default.
- W2085997191 cites W2963350927 @default.
- W2085997191 cites W3103855587 @default.
- W2085997191 cites W4230173782 @default.
- W2085997191 cites W4233548550 @default.
- W2085997191 cites W4234656298 @default.
- W2085997191 cites W4235080073 @default.
- W2085997191 cites W4237630666 @default.
- W2085997191 cites W4251575809 @default.
- W2085997191 doi "https://doi.org/10.1016/j.jmva.2013.08.010" @default.
- W2085997191 hasPublicationYear "2013" @default.
- W2085997191 type Work @default.
- W2085997191 sameAs 2085997191 @default.
- W2085997191 citedByCount "27" @default.
- W2085997191 countsByYear W20859971912013 @default.
- W2085997191 countsByYear W20859971912014 @default.
- W2085997191 countsByYear W20859971912015 @default.
- W2085997191 countsByYear W20859971912016 @default.
- W2085997191 countsByYear W20859971912017 @default.
- W2085997191 countsByYear W20859971912018 @default.
- W2085997191 countsByYear W20859971912019 @default.
- W2085997191 countsByYear W20859971912020 @default.
- W2085997191 countsByYear W20859971912021 @default.
- W2085997191 countsByYear W20859971912022 @default.
- W2085997191 countsByYear W20859971912023 @default.
- W2085997191 crossrefType "journal-article" @default.
- W2085997191 hasAuthorship W2085997191A5062722872 @default.
- W2085997191 hasAuthorship W2085997191A5063510610 @default.
- W2085997191 hasAuthorship W2085997191A5075389310 @default.
- W2085997191 hasBestOaLocation W20859971912 @default.
- W2085997191 hasConcept C102366305 @default.
- W2085997191 hasConcept C105795698 @default.
- W2085997191 hasConcept C11413529 @default.
- W2085997191 hasConcept C127413603 @default.
- W2085997191 hasConcept C134306372 @default.
- W2085997191 hasConcept C147493315 @default.
- W2085997191 hasConcept C154945302 @default.
- W2085997191 hasConcept C185592680 @default.
- W2085997191 hasConcept C198531522 @default.
- W2085997191 hasConcept C2776214188 @default.
- W2085997191 hasConcept C28826006 @default.
- W2085997191 hasConcept C33923547 @default.
- W2085997191 hasConcept C41008148 @default.
- W2085997191 hasConcept C41608201 @default.
- W2085997191 hasConcept C43617362 @default.
- W2085997191 hasConcept C51820054 @default.
- W2085997191 hasConcept C529865628 @default.
- W2085997191 hasConcept C62799726 @default.
- W2085997191 hasConcept C78519656 @default.
- W2085997191 hasConcept C80884492 @default.
- W2085997191 hasConceptScore W2085997191C102366305 @default.
- W2085997191 hasConceptScore W2085997191C105795698 @default.
- W2085997191 hasConceptScore W2085997191C11413529 @default.
- W2085997191 hasConceptScore W2085997191C127413603 @default.
- W2085997191 hasConceptScore W2085997191C134306372 @default.
- W2085997191 hasConceptScore W2085997191C147493315 @default.
- W2085997191 hasConceptScore W2085997191C154945302 @default.
- W2085997191 hasConceptScore W2085997191C185592680 @default.
- W2085997191 hasConceptScore W2085997191C198531522 @default.
- W2085997191 hasConceptScore W2085997191C2776214188 @default.
- W2085997191 hasConceptScore W2085997191C28826006 @default.
- W2085997191 hasConceptScore W2085997191C33923547 @default.
- W2085997191 hasConceptScore W2085997191C41008148 @default.
- W2085997191 hasConceptScore W2085997191C41608201 @default.