CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.08 - August
Issue No.08 - August (2009 vol.31)
Dan A. Alcantara , University of California, Davis, Davis
Owen Carmichael , University of California, Davis, Davis
Will Harcourt-Smith , American Museum of Natural History, New York
Kirstin Sterner , New York University, New York
Stephen R. Frost , University of Oregon, Eugene
Rebecca Dutton , University of California, San Francisco, San Francisco
Paul Thompson , University of California, Los Angeles, Los Angeles
Eric Delson , American Museum of Natural History and Lehman College, City University of New York, New York
Nina Amenta , University of California, Davis, Davis
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.
Feature representation, size and shape, life and medical sciences.
Dan A. Alcantara, Owen Carmichael, Will Harcourt-Smith, Kirstin Sterner, Stephen R. Frost, Rebecca Dutton, Paul Thompson, Eric Delson, Nina Amenta, "Exploration of Shape Variation Using Localized Components Analysis", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 8, pp. 1510-1516, August 2009, doi:10.1109/TPAMI.2008.287