Exploration of Shape Variation Using Localized Components Analysis

Authors:
Dan A. Alcantara;Owen Carmichael;Will Harcourt-Smith;Kirstin Sterner;Stephen R. Frost;Rebecca Dutton;Paul Thompson;Eric Delson;Nina Amenta
Affiliations:
University of California, Davis, Davis;University of California, Davis, Davis;American Museum of Natural History, New York;New York University, New York;University of Oregon, Eugene;University of California, San Francisco, San Francisco;University of California, Los Angeles, Los Angeles;American Museum of Natural History and Lehman College, City University of New York, New York;University of California, Davis, Davis
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

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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.