Exploration of shape variation using localized components analysis

Dan A. Alcantara, Owen Carmichael, Will Harcourt-Smith, Kirstin Sterner, Stephen R. Frost, Rebecca Dutton, Paul Thompson, Eric Delson, Nina Amenta

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.

Original languageEnglish (US)
Pages (from-to)1510-1516
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume31
Issue number8
DOIs
Publication statusPublished - 2009

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Keywords

  • Feature representation
  • Life and medical sciences
  • Size and shape

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Software
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Alcantara, D. A., Carmichael, O., Harcourt-Smith, W., Sterner, K., Frost, S. R., Dutton, R., ... Amenta, N. (2009). Exploration of shape variation using localized components analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(8), 1510-1516. https://doi.org/10.1109/TPAMI.2008.287